Cite this asWu J, Zha P (2023) Flawed foundation is the root cause of failure of medicine and precludes cures for chronic diseases. Glob J Cancer Ther 9(1): 001-019. DOI: 10.17352/2581-5407.000050
Copyright License© 2023 Wu J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Modern (science-based) medicine adopted four presumptions when it evolved from ancient experienced-based mind-body medicine. To understand its failure in finding cures for chronic diseases, we examined four presumptions and found that statistical population of health properties does not exist for most research purposes, mathematical models are misused to model intensive properties, synthetic drugs are inherently more dangerous than nature-made medicines under their respective application conditions, and reductionist treatments are inferior and inherently dangerous. We found that clinical trials are valid only for research where the treatment effect is much stronger than the total effects of all interfering or co-causal factors or errors introduced by misused mathematical models can be tolerated. In all other situations, clinical trials introduce excessive errors and fail to detect treatment effects or produce biased, incorrect, or wrong results. We further found that chronic diseases are the manifestation of small departures in multiple processes attributes in distinctive personal biological pathways networks, that modern medicine lacks the required accuracy for accurately characterizing chronic diseases, and that reductionist treatments are good at controlling symptoms and safe for short-term uses. For all stated reasons, as long as modern medicine continues relying on flawed presumptions, it can never find predictable cures for chronic diseases. By implication, predictable cures to chronic diseases are adjustments to lifestyle, dietary, emotional, and environmental factors to slowly correct departures in process attributes responsible for chronic diseases.
The systematic failure of medicine in chronic diseases was extensively discussed as early as 1875  and is often the subject of critique by the media [2-4]. As of today, most chronic diseases have no predictable cure in medicine . Population-based treatments have failed in cancer, heart diseases, mental disease, etc. [6-8]. Chronic diseases are the biggest economic burden in the U.S.  and are predicted to consume about $3.5 trillion by 2050 . From medical performance, we see two distinct patterns: treatments for acute diseases are successful, but treatments for chronic diseases and cancer consistently fail. Thus, we suspect that the failure in chronic diseases must be of a systematic nature that might have precluded cures for chronic diseases.
In our prior study, we found that controlled trials are improper methods for studying weak health factors when many interfering factors (equivalent to covariates in statistics) normally exist in clinical trials . Our findings support the conclusion that all controlled clinical trials are biased . Our prior model study shows a common scenario where each weak treatment or factor cannot be resolved or accurately determined if it is interfered with by at least one to thousands of other factors that have similar degrees of effects. If a weak factor is studied in a clinical trial, it is improperly rejected as an experimental error. Most assumptions used in clinical trials and statistical analysis have not been considered .
To understand how weak factors affect disease outcomes, we need to examine biological pathways and disease-controlling mechanisms. We have shown that randomized controlled trials do not have the power to overcome sensitivity limits . This insufficient-accuracy problem cannot be seen from the outcomes of clinical trials. When the validity of the research model is challenged, such a challenge cannot be resolved by examining the outcomes of research using the model. Moreover, due to the complexity of health problems and the massive number of interfering factors that exist in clinical trials, it is impossible to find problems by examining the data of clinical trials. To find research model flaws we are required to consider all kinds of evidence other than clinical trial outcomes.
Presumptions are part of the foundation of medicine and are taken as truth so that their validity has never been questioned or examined. When medicine evolved from ancient medicines into modern medicine, it changed natural medicines into synthetic drugs, changed populations into statistical populations, introduced mathematical models as universal tools for medical research, and used the binary scale to model health properties. To find the cause of the systematic failure in chronic diseases, we will examine all presumptions and assumptions. The oldest presumption was that “medicine can cure disease”. While this presumption existed in early history, a medicine used in the Yellow River Civilization is not the same as “medicine” prescribed in doctor offices today. We will consider what is wrong with using a population to study chronic diseases and what problems mathematical models can create. In addition, we will examine each of the assumptions used in clinical trials and statistical analysis to show additional flaws from biological points of view.
We collect from medical literature published data and research findings that tend to support or refute challenged presumptions and assumptions. We rely on data from four sources: one of the sources is research findings that establish the existence of interfering factors and their degrees of effects. Due to an extremely large set of study findings, we will cite only selected references, and treat others as common knowledge. We must use this unique approach because no single set of data or any particular findings can ever resolve this challenge. This is why conducting one or more experiments is meaningless because the data of each study is like a drop of water in a bucket. The second source of data we rely on is media stories, reports from health care providers, personal stories, and observations. When the validity of controlled trials is under challenge, we give more weight to those sources of evidence. Based on our prior studies , we ignore negative findings from controlled trials directed to weak factors.
Other data considered include biochemical pathways, cellular or structural data, disease mechanisms, host responses to stressors, immune responses, factor-factor interactions, organ-organ interactions, rational explanations based on body structural compartment, rate balance among different biological pathways, and balance between disease process and healing process. Due to the large scope, we do not cite all contributors directly. The third source of data is data from simple mathematical models to refute or support concepts or mechanisms. Such a method is used only to show that clinical trials with current data analysis can produce inaccurate, biased, or wrong results, but not used to establish that the use of the mathematical method is right. As we show, the use of mathematical models is often improper if the research purpose is assessing treatment benefits and finding cures. If the use of the mathematical models is refuted, clinical trials, as a research method, fall for this reason without regard for the propriety of mathematical models. The fourth source of data is the performance data in treating diseases. However, since most performance studies are based on clinical trials by various degrees, we must read them to offset potential inaccuracies. In general, the treatment benefits of weak factors are underestimated based on our prior study [9,10] and obvious logic.
To show why clinical trials are invalid for most research purposes, we studied its development history provided in the medical literature . To understand mathematical models, we study biological pathways and their interactions, the multiple interactive disease mechanisms, factor-factor interactions, and the structural effects of tissues and organs, etc. In addition, we will show that the treatment unit additivity assumption and an implied random error assumption fail to hold in nearly all clinical trials for studying chronic diseases.
The development history of clinical trials reveals that clinical trials were developed by adding components piece by piece by different contributors over several centuries . When the clinical trial was first used, there was no need to examine the statistical population because statistical analysis was not a part of the analysis method for clinical trials. “Population” used in early human history just means a collection of members, and early medical researchers naturally used population to study diseases because it always created a false impression that a treatment capable of curing more persons must be better than one that does not. This is still a reason for convincing researchers today. In the early days, there was never a need to examine the population of biological properties or health and disease properties. The controlled trial on scurvy conducted by James Lind in 1747 contained most elements. By 1946, all components of randomized controlled trials had been added. It is fair to infer that the clinical trials had gained general acceptance before the 60’s  without using statistical analysis. Before about 1980s, medical researchers did not know the massive biological properties concerning disease initiation, development, and reversal, the complex human immune system, and the role of the Central Nervous System. They did not have a vantage to see how personal genomes, environmental factors, emotional states, etc. affect health and disease properties. It was natural to presume that any health property in different people is similar so that the values of the property from different people can be treated as a statistical population.
Decades after clinical trials gained general acceptance, researchers started looking into the human genome, biochemical pathways, environmental factors, lifestyle factors, emotional problems, etc. The effects of a large number of primitive factors on diseases have been established by tens of thousands of studies mainly after 1980 [See some references in Section E]. Even though a good portion of studies is conducted by using a population approach, affirmative findings in those studies suffer inaccuracy by various degrees. Nevertheless, those positive findings have firmly established that differences in health and disease properties cannot be treated as random errors, and there is no statistical population as far as health and disease properties are concerned. Unfortunately, massive discoveries have not prompted medical researchers to revisit the presumption of a statistical population in the last few decades. We have shown that the effects of massive interfering factors are responsible for trial outcome uncertainty. When the nature of interfering factors was not understood, it was natural to attribute trial outcome uncertainty and conflicting findings to experimental errors. It is natural to try to solve this problem by using misapplied statistical analysis.
Misuse of statistical analysis in clinical trials is clearly reflected in the development history of statistics. Statistical analysis was added to clinical trails as one of the few last components. The origins of statistical theory lie in the 18th century, but improved experimental design, hypothesis testing methods, etc. were developed in the 1910s and 20s by William Sealy Gosset and Ronald Fisher, and further refinements were made in the 1930s . Hypothesis tests are used to determine whether positive outcomes in clinical trials are caused by the treatment effect or due to uncontrollable experimental error. The use of hypothesis tests in clinical trials started centuries after the initial use of clinical trials and more than a decade after the formation of modern clinical trials. Statistical analysis was added as an additional analysis step to clinical trials from the 30s to about 60s. When the statistical analysis was added, the traditional concept population was changed into a statistical population. No published study has seriously discussed whether the health properties of human beings can be treated as a statistical population and their numbers can be added and divided like fungible properties.
Past studies including those done by Altman, Senn, Zhao, and Berger [13-18] have made a presumption that any health properties such as survival times, process attributes such as conversion rate and intermediate concentrations, etc. in human bodies can be treated as a statistical population [13-18]. They did so without exploring the effects of all interfering and co-causal factors that normally exist in clinical trials. This presumption had been accepted for several decades before. It had been beyond challenges. By using this presumption, even extremely complex health and disease properties such as survival time and emotional health can be studied like statistical population, where outcome uncertainty can be attributed to random processes like rolling a dice or blowing colored balls from a lottery machine. After examining disease mechanisms and existing risk-disease data, we found that no disease happens like flipping a coin and blowing colored balls.
Due to historical development reasons, early researchers could not pay attention to biological properties and their effects on health properties. Flaws in those studies can be summarized as follows: First, they made a presumption that disease and health properties can be studied like drawing events and that the health properties of human beings in a treatment group can be treated as a statistical population. They then made an assumption that disease or health properties can be mathematically added up and divided to yield a mean for the presumed population. In doing so, they made another assumption that health properties are fungible and exchangeable, and all uncontrollable interfering factors do not exist or can be neglected as random errors, and failed to examine whether treatments have different effects on different persons, how interfering factors affect health properties, and how their plus and minus effects distort analysis outcomes.
In the early years, they did not have the vantage to see the irrefutable evidence that most so-called errors are not truly random errors that are seen in statistical trials, but a combined effect of hundreds to thousands of interfering or co-casual factors. They did not consider abundant evidence that treatments often have different levels of effects and different-sign effects on different persons, that the magnitude of measurement errors due to interfering factors can be larger than treatment effects; that the interaction between a treatment and any of potential interfering factors is distinctive in each person; that interfering factors can distort treatment effects by their positive and negative effects, with sufficient magnitudes to distort trial outcomes, and that personal biological properties are distinctive. Without considering external evidence, they were not in a position to compare the effects of treatment with the effects of interfering factors and naturally attributed all differences among individual persons to experimental errors.
Past studies by Altman, Senn, Zha, and Burger [13-18] share several common errors: They treated the health and disease properties of human beings as a population. They assumed that the differences among individual persons happen like those in statistical sampling, but never discussed external evidence about health properties such as process attributes . By failing to look into the nature of all interfering factors, they bundle all contributions caused by different process attributes in the biological pathway networks into experimental errors. They concluded that there is no need to be concerned about the baseline balance. They never proved that the health properties of people can be treated as statistical populations. If statistical analysis can fix the overwhelming problems, we could reach absurd conclusions that controlled trials have the power to resolve contributory effects of any weak factors; valid scientific research does not depend on separation method and detection technologies; and research sensitivity limits can be overcome by running bigger clinical trials followed by doing statistical analysis. Each of those conclusions must fall automatically.
What is wrong is that the “statistical population” has been taken for granted for any research purpose. This is reflected nearly in all medical studies that never even attempt to determine whether a statistical population of a health property exists for intended research purposes. When a clinical trial is used to study a chronic disease, it is required that all persons must be similar in their chances of getting or resisting the same disease. Due to the massive interfering factors in clinical trials, clinical trials tend to produce different outcomes. Early researchers could not understand the sources of uncertainty and naturally assumed that trial uncertainty is caused by experimental errors beyond human control. Naturally, statistical analysis was used to solve this problem. When statistical analysis was added, the population was silently changed into a statistical population. Medical researchers have made a presumption that a statistical population exists for any health property and any research purpose. This is evidenced by the widespread abuse of statistical analysis in medical research publications.
While hypothesis tests are not wrong for all research purposes, they can address only experimental uncertainties that are truly caused by uncontrollable random errors that happen like flipping a coin, blowing colored balls out of a lottery machine, or rolling a dice. The massive medical findings have firmly refuted that trial outcome uncertainty is caused by uncontrollable random processes. Implied requirements for classical statistical trials are that the coin must have identical weights on two sides to have the unbiased chance to produce each outcome, all numbered balls in a lottery chamber must have the same weight, same shape, same size, and uniform internal density, and the dice must be a cubic with the same area on all six faces, and has a same density at every inner locality within the dice. If the rim or density near two surfaces of a coin is altered, it will introduce systematic errors that cannot be treated as random errors. A coin with an altered feature may produce an outcome ratio other than 1:1. The ball sizes and densities among different balls can be changed to result in different outcome probabilities. In clinical trials, systematic errors cannot be determined from trial results. Systematic biases cannot be corrected without understanding the nature of the biases. The systematic biases can be established in reality by external evidence other than trial results. All interfering factors in clinical trials can have systematical impacts on trial outcomes. The inability to control interfering factors is not the reason to ignore their existence.
A vast number of primitive factors such as nutrition, toxins, heavy metals, exercise, emotional issues, etc. are not really uncontrollable. Each of those factors is weak and hidden among the rest of the factors. It is like a situation where the effect of each factor cannot be determined but the collective effects of all factors are responsible for diseases. Even intermediate factors such as glucose or triglyceride levels in the blood can be altered by adjustment to lifestyle. None of those factors work like an uncontrollable driving force that makes a spinning coin take one particular outcome.
Misuse of statistical analysis could not remove the trial outcome uncertainty. Great uncertainty in trial outcomes provides great room for the manipulation of experiments. Instead of investigating the inherent flaws, medicine has tried to address this uncertainty problem by controlling selection biases and conflict interests as remedies. Thus, we see massive ethical regulations established after 1946 . While selection biases can cure uncertainty caused by identifiable factors such as age, sex, overall health, disease stages, etc., they cannot do away with outcome uncertainty caused by a large number of other uncontrolled interfering factors. Conflict-of-interest measures can never do away with outcome uncertainty except that it has become a scapegoat for the flaws of clinical trials. Such measures create massive administrative burdens which are rarely seen in other fields such as bridge design, aviation, automobile, etc.
While the population concept can be used for various purposes, the population, as used in statistical analysis in clinical trials for diseases, can be refuted by relying on observed health properties and known analytical data. Boys, girls, men, women, healthy persons, persons with unidentified diseases, etc., are expected to have different baselines. For example, a healthy young person may have a baseline survival time of five thousand days while an old patient may have a baseline survival time of fifty days. Among the persons in a trial, besides the term “person”, they are different in biological age, physical strength, shape and look size and weight, etc. They differ in physical check-up data and laboratory analysis data [20,21]. Differences in local concentrations of intermediate compounds of some biological pathways in tissue cells between different persons could be more striking even though few studies were done to understand such differences at the cellular level. Even if assuming that diseases were realized in a manner like blowing human physical entities out of a lottery machine, some persons might be “drawn” at much higher probabilities. No population would meet statistical distribution except by approximation in studies concerning body weight and head count. In most clinical trials, the baseline health property for each person cannot be accurately measured and determined. This present inability is not a valid basis for treating differences in health properties as random errors.
As a general rule, when a treatment in a trial is sufficiently strong while experimental errors are relatively small, two experiments with two measurements in each would be enough without using statistical analysis. Statistical analysis is never required in most experiments in analytic chemistry. In a drug trial with the endpoint being survival time, real experimental errors are small because survival time, treatment dates, and drug doses can be determined and recorded accurately. However, for the interfering or co-causal factors, clinical trials repeated in the same condition are expected to produce consistent results without the need to use statistical analysis. In a trial involving a poorly defined treatment such as a stress-relieving method, part of the outcome uncertainty is caused by the uncertainty in treatment definition; and part of the outcome uncertainty is caused by interfering factors. Statistics is not a suitable method for taking care of interfering factors and definition uncertainty of treatments.
We will discuss several model problems that make population presumptions fail.
In Table 1, the treatment has a fixed effect of 1 on all persons. One interfering factor in row 2 has positive or negative effects and raises the variances of the observed data in row 3. The net interfering factor in row 2 could be viewed as a control. If no interfering factor, all values in row 2 would be near zero, all measured values would be 1, and the mean could be determined without any uncertainty. The interfering factor dramatically raises the variances of the treatment in row 3, and thus raises the threshold of rejecting the null hypothesis. The trial most probably fails to find the true benefit of the treatment. When hundreds of interfering factors exist, the variances seen for the treatment must be larger even if the interfering factors follow complex or unknown distributions.
In Table 2, a single strong interfering factor dramatically increases the variances of the observed data. The massive variations of the interfering factor find their way into the values of the treatment group relative to a control. Due to the massive variances between individual persons within the treatment group, the true treatment effect in row 1 may be completely hidden in the interfering effect. Although the treatment’s mean is detected as unbiased, the enlarged variances will result in a much higher threshold for rejecting the null hypothesis. Based on other observational data, we must say that the variances from different persons are very high and result in failure to reject the null hypothesis. We suspect that strong interfering factors are very common in studies intended to study weak and slow-delivery environmental and lifestyle factors. In such situations, clinical trials always produce false negative findings.
In Table 3, a single strong interfering factor has a biased effect on the measured health properties of both treatment and control. Those kinds of interfering factors include fears, development stage, aging, seasonable effects, exposure to bad news, etc., that strike all subjects in both the treatment and control groups. They can dramatically increase the variances of the observed data. In addition, they also move the measured values as well as the mean to the negative side. Even though the treatment has a net effect of 1 on each person, the observed data may be still negative. In addition, aging, development stage, seasonal factors, or exposure to bad news may interact with the treatment. A treatment may have curative benefits, but negative news may make all subjects so sad that the news might have suppressed the immune system or the whole body’s health. Due to the massive variances and negative interactions, true treatment effects are most probably rejected as errors.
In clinical trials, there are hundreds to thousands of interfering factors. Their variances can be added up for well-known distributions . For unknown distributions, the final variances depend on the number of interfering factors and their effect on variances within the treatment group. This results in the systematic rejection of weak treatment effects.
The total treatment’s effect for the treatment group is the sum of all treatment effects on all persons (Ignoring the problem in additivity for the time being). A large number of interfering factors interact with the treatment. If they interact with the treatment in an unpredictable way, the average treatment effect is meaningless. The nature of interactions is determined by the treatment OR interfering factors. For example, a calcium supplement is predicted to benefit bone health, but high sodium daily intake promotes calcium loss. The sodium’s effect on calcium balance depends on persons who have different sodium intakes. Similarly, exercise can beneficially affect innate immunity, acquired immunity, etc., and people vary in doing exercises. Exercise can dramatically raise the beneficial role of other treatments such as nutrition and detoxification of heavy metals intended to improve the immune system. Upper and downshifts of baselines cannot be determined for a specific person. Thus, the knowledge of baseline upper or downshifts by interfering factors can provide a better strategy for formulating treatments.
In the above example, a simple mathematical model is used to characterize the treatment effect and its interactions with interfering factors. However, accurate interactions cannot be characterized accurately due to multiple layers of complex healing and disease mechanisms.
Each person is a unique being by genome . The typical difference between the genomes of two individuals was estimated at 20 million base pairs (or 0.6% of the total of 3.2 billion base pairs) . Moreover, even identical twins can become different beings by epigenetic changes that have an effect of turning on or off gene expressions [23,24]. Each disease like cancer is a distinctive product of personal genome, diet, living environment, etc. [25,26].
Metals, including lead, cadmium, mercury, arsenic, chromium, copper, selenium, nickel, silver, and zinc, and other metallic contaminants including aluminum, cesium, cobalt, manganese, molybdenum, strontium, and uranium are found in living organism, plants, contaminated vegetables, industrial materials and polymers, soil and land resource, and air and water . Most heavy metals such as aluminum, arsenic, beryllium, cadmium, lead, mercury, nickel, and radium increase the risks of cancers including lung, kidney, liver, stomach, intestines, bladder, colon, gastric, nasopharyngeal, pancreatic, breast, gallbladder, esophagus, prostate, testes, gastrointestinal skin cancer and non-Hodgkin’s lymphoma [67-69]. Exposure to arsenic, lead, cadmium, and copper is associated with an increased risk of cardiovascular diseases and coronary heart disease [70,71]. Heavy metals can damage cells , disturb the Redox balance [72,74], and suppress the immune system often at very low concentrations [73,74]. Many heavy metals can damage the liver, kidneys, brain, and nerves . Alteration of homeostasis of metals could cause the overproduction of reactive oxygen species and induce DNA damage, lipid peroxidation, and alteration of proteins, thus increasing the risks of developing brain tumors . Metals such as lead, mercury, cadmium, and arsenic may be important contributors to neurodevelopmental disorders and disabilities . The findings in those studies firmly establish that heavy metals can cause specific diseases, but they must be viewed as having global adverse health effects because they can interfere with enzymatic reactions that control reaction rates of all biological pathways.
Inorganic and organic substances can have adverse health effects. Sodium, the most common flavor is the number-one silent worldwide killer due to its role in raising blood pressure . Habitual dietary salt intake is positively associated with the risk of gastric cancer . Besides cardiovascular diseases, high salt intake increases risks of gastric and some other cancers, obesity, Meniere’s disease, worsening of renal disease, triggering an asthma attack, osteoporosis, exacerbation of fluid retention, renal calculi, etc. . High sodium intake is associated with obesity . Moderately high salt intakes affect calcium metabolism and bone health . Reduction of sodium intake can reduce both systolic and diastolic pressures . Exposure to common quaternary ammonium disinfectants may decrease fertility based on animal models [83-85]. Hydrogen peroxide may cause poisoning . Lack of exercise and physical inactivity are found to be the substantial causes of chronic diseases [87-88]. From the benefits of exercise on cancer survival [89-100], it is expected that reduced exercise and increased inactivity have adverse impacts on survival among cancer patients. People have different organ reserve capacities [101-103], which are presumed to be the most important factor that affects patients’ ability to survive diseases.
Available spaces in the thoracic cages affect their ability to accommodate tissue swelling in the lungs . Obesity is found to be a high-risk factor for COVID-19 disease [105,106]. Information stored on the CNS neurons is different, and it, like computer programs, affects emotional health and CNS regulatory functions in the body. The lack of medical findings is not a reason to deny its role and importance. Full details of those factors can be presented only in a searchable database. Even environmental factors such as oxygen , humidity , and temperature  affect immunity and pathological responses to infection. Massive organic compounds, industrial materials, industrial chemicals, pesticides, herbicides, fungicides, etc. will be discussed elsewhere.
Many factors exhibit non-linear complex effects and may interact with each other. The CNS interacts with bone, marrow, and the micro-environment [106,111]. Enteric microbiota and central and enteric nervous systems interact through the gut-brain axis [112,113]. Sodium also exhibits different effects under different use conditions. High salt (4% NaCl, as well as 1% NaCl, enriched tap water feed mice for 2 weeks) inhibits tumor growth by enhancing anti-tumor immunity  contrary to the long-term adverse effects. Like glucose level that has both good and bad roles, sodium’s short-term effect may be realized by influencing blood viscosity and fluid ionic strength while its long-term effects are most probably realized by affecting blood pressure and the vascular system. Any factor affecting viral diseases could also depend on a large number of other factors that affect innate immunity, host responses, acquired immunity, micro-circulation, and structural features of target tissues. The cited findings provide irrefutable proof that none of the interfering factors can be ignored in the mission to find cures.
In modern medicine, another presumption is that every health problem can be represented by a mathematical model. Now, a supermajority of medical studies includes statistical analysis. We refute this presumption.
Mathematical models cannot model complex interactions of health properties and primitive factors. Health properties such as glucose level, triglycerides levels, oxygen saturation, etc. may work as influencing factors for other health and disease properties. They also affect other high-level health properties such as disease risks, death rates, survival time, etc. Due to complex interactions, we found that most health properties must be multiple complex functions of a large number of primitive factors. There is no best nutritional profile, no best diet, no best copper intake, no best environment, etc. because the effect of each factor also depends on other factors and personal activities. There are no objective criteria for determining what is best. There is no best amount of exercise, and nor best kind of exercise for all people in a population. Even for a given person, there is no static best value. An imagined best value may exist only under certain conditions by using arbitrary evaluation criteria and must change with age, health condition, activity levels, emotional health, and other personal, environmental, and lifestyle factors. The notion of best value such as the best sleep duration for a population is flawed. The linear models used in statistics can model only simple properties like crop weights and production yields when the research purpose concerns a fungible property.
The unique nature of process attributes implies that health properties are not the types of properties for mathematical operations. Moreover, interactions between disease initiation and multiple layers of disease defense mechanisms also refute this assumption completely. Disease mechanisms are further influenced by a large number of primitive lifestyle and environmental factors. Clinical trials can produce unpredictable and inconsistent results due to the effects of influencing factors at different layers. A factor promoting diseases may be found to have no effect if a strong defensive mechanism in most human subjects can overcome initiated diseases; and in another trial, the same factor promoting disease initiation may be found to be a strong controlling factor if the defensive mechanism in most subjects is compromised.
Most health properties are process attributes such as conversion rates, the concentrations of intermediates, or the matrix of those things. In the treatment group, a mean determined by mathematical averaging can represent none of the members in the treatment group. If a treatment is found to have positive effects over a control group, what is proved is that the treatment has sufficiently positive effects on the members of the treatment group over the control. Such a positive value can be detected if the treatment effect is stronger than the sum of all interfering factors in the treatment group, the treatment produces beneficial effects on more persons than it produces adverse effects of the same degrees on others within the treatment group, or the treatment has a net beneficial effect on the treatment group over the control for whatever reasons. It does not prove that the treatment is effective for the treatment group, is effective for treating the disease, or is the best for all persons with the disease.
The notion that “an average represents a population” is generally wrong unless a statistical population can be established by independent evidence. In politics, number-based representation is a principle imposed by will, but not natural law. In a statistical population, the members must share enough similarities so that the members can be used to investigate a treatment. This requirement is entirely relative to the investigation purpose. A computed average can represent a population only if a statistical population actually exists. The existence of a statistical population cannot be proved by the mathematical operation itself, reasonable data pattern, or a computed average value. Mathematics can be used to determine the average weight of a sesame seed and a fighter carrier or the average heart output of an elephant and a bird. Such averages can represent neither the weights of the sesame or the carrier nor the heart outputs of the elephant or the bird. While the values in those two examples are extreme, similar data values do not provide a basis for finding a statistical population. Similar reaction rates in certain tissue cells in tigers and turtles do not make the rates a statistical population (even though the turtle’s mean may be used to estimate the tiger’s mean in practice). It is possible that apple tree data might nicely fit into human data purely by accident.
The existence of a statistical population must be established by examining individual members and the purpose of the investigation. If an identical nutrient intake has a beneficial effect on one person but a toxic effect on another person, the average value, which has the same value, does not represent a beneficial effect for both, nor a toxic effect for both. On the contrary, apple, orange, and plum in a compartment mixture could be treated as a reasonable statistical population if the investigation purpose is to estimate packaging volume. Even abstract concepts may become a statistical population if their differences do not defeat the investigation purpose. Similarly, deformed coins, irregular balls, and non-cubic dice with varying inner local densities cannot be used in drawing sampling for classical statistical trials. In the vast medical research articles, research purposes and accuracy requirements have been ignored. This single error makes many study findings meaningless.
The permissible use of mathematical operations for population-based study depends mainly on the purpose of the research. Grain weights may be added and divided if the research purpose is to study grain supply and demand. In this situation, grain weight is fungible because mathematics does not differentiate sources just like market demand. However, if the research purpose is to increase individual seed weights by using a new treatment, grain weight is not a fungible property. We must consider if the treatment has the same effect on each seed. If the same treatment can have different effects on different seeds with different genetic compositions, a mathematical model that regards the treatment as having the same effect must fail. Lifespan is partially controlled by complex biological pathways, and thus is not fungible: extending 20 years for a boy is not the same as extending 20 years for an elderly person. However, survival time could meet the statistical population if the research purpose is to determine total community life spans for the purpose of getting a financial reward under a lifespan-based reward program. If a mathematical model treats positive and negative effects of influencing factors as experimental errors, the errors must be sufficiently smaller than the treatment effect so that study validity can be justified by approximation. Based on this rationale, mathematical operations cannot be used to find the best treatments for persons who have distinctive biological properties.
Mathematical averaging of process attributes is improper also because most process attributes have no standalone meaning. One class of properties is intensive property which reflects the local physical property of a system. Examples of intensive properties include temperature, pressure, refractive index, and density. Extensive properties such as mass and volume are additive. Temperature is not additive because heat absorbed at different temperatures would be different, and temperature at different systems such as water and gas means completely different things. Process attributes and health properties are similar to intensive properties. A Civic uses fuel at the rate of 1 gal/time (where time is a suitable time unit) and an Accord consumes fuel at 2 gal/time. Their average would be 1.5 gal/time. This number may indicate the average usage of fuel from fuel supplies. However, this number cannot be used to study the performance of the cars because the performance of each car depends on a large number of other variables such as driving distance and weight of carried goods. The average, 1.5 gal/time, has no meaning if it is viewed out of context. Fuel injection rate can be evaluated only against criteria such as shipping weight and running distance. We can infer that all process attributes such as fuel injection rate, coolant flow rate, heat dissipating rate, etc. from individual cars or planes cannot be added and averaged across the different models, and then applied to any specific unit.
Direct mathematical average is proper only for fungible properties such as crop weight and production volume. For such properties, the significance of each unit of weight or volume does not depend on other variables. However, direct averaging without using weights is improper for computing the average for alcohol of 99% purity and alcohol of 30% purity. The net weight of alcohol depends on their purity which is an additional variable. For nearly all health properties, the significance of process attributes always depends on other variables. Mathematical operations used in classical probability trials do not violate the fungible requirement. In probability trials, events are defined accurately. The appearance of a numbered ball, a dice position, or a coin face is not subject to additional variables. Each outcome has the same significance as any of the other outcomes. That is the basis for adding them up to get a sum. Observing examples in statistical books, we found that an intensive property may be used as a statistical population only for the same system or similar systems. For example, the daily production rate of a machine can be added up and averaged for different days because all other variables are fixed and thus the number of product pieces is the only variable. Whether production rates from different machines can be summed and averaged depends on the purpose of mathematical operations.
Contrarily, process attributes are generally not the kind of properties that can be summed up and divided. The specific values of process attributes do not have standalone meanings. They are incapable of determining system performance like health or disease states. Glucose level, a process attribute, affects health by interacting with other factors or variables. When the glucose level is low, it is vital to survival. If it increases, its benefit reaches a plateau. Further, an increase in the glucose level will cause negative effects by damaging the vascular system. Thus, the 15 mg/liter on the low end and 15 mg/liter on the high end have different benefits even for the same person; and 10 mg/liter in diabetes patients and 10 mg/liter in hypoglycemia patients have different meanings. Averaging glucose levels for diabetes patients and hypoglycemia patients would result in a “healthy” mean, which is clearly contrary to reality. We can find that all process attributes share this same problem.
Any process attribute, as well as a unit change to an attribute such as glucose level (mg/liter), red blood cells (no/liter), white blood cells (no/liter), enzyme activity (in any units), etc., have no standalone meaning unless it is considered for a specific person under a set of specific conditions. Thus, a computed mean of any health property has no meaning. If the reaction rate of a specific biological pathway in a person is X while that for another person is 2X, the mathematical average can represent neither. Intermediate concentrations also have no meaning. A low glucose concentration would imply low conversion speed only if the rate constant for the biological process is the same. However, in reality, 110 mg/liter in an obese person may reflect an even lower conversion speed than 70 mg/liter in a young person. Similarly, the rate constant or activity level of an enzyme has no meaning unless it is considered in context. The high concentration in an obese person might be caused by an excessively slow conversion rate so that more of the absorbed glucose is backed up in the blood. In addition, the net conversion rate must be influenced by the physical structural features of the body.
Each process attribute in the biological networks [116-118] of a person is distinctive and this nature bars approximations. Given the long development time of chronic disease, departure in any process attribute in the biological network is very small. Thus, the computed mean cannot be imposed on any individual person because the mean must be different from the corresponding value for the person. Based on the above discussion, we find that all process attributes have non-linear, complex effects on personal health and that their effects on personal health depend on many other personal factors. Personal health values cannot be added up across different persons except in situations where research purposes can tolerate such errors.
Many large-scale clinical trials such as the TAILORx trial  reveal misuse of the representation principle. It attempted to get better “representation” from people by running multiple national trials. Since findings from clinical trials always had some kind of average of personal numbers, they cannot represent a supermajority of the persons other than lucky persons whose numbers luckily fall on the average (which may happen by the chance of winning a lottery). The average is not the optimal value of any person in the trial subjects. Since the mean of a health property derived from a population cannot represent individual persons, a treatment based on such health property cannot be valid for any of the participant persons except the abstract person who does not exist. There is no basis to find that such a treatment is best for other patients outside the trial. The flawed logic is that the validity of the treatment for persons in the U.S. depends on how good the treatment is for persons in Brazil.
3. Use of the binary scale and characterizing properties by categories: Another problem arises from using the binary scale. Most health properties are continuous properties except for a few things like gender and death. Many health properties actually exhibit 0 and 1 states, with 1 state further comprising values in a non-linear continuous profile. One obvious example is exposure to a virus. Exposure can be classified as no and yes. Among exposures, infection risk would depend on the number of viral copies exposed. However, nearly all health properties or process attributes of biological pathways are continuous. They differ in amount or degree. Conversion of such properties into the binary scale introduces excessive errors. By common sense, digitizing a sound with a two-bit digital scheme can introduce great distortions. Conversion of data into the binary scale can introduce as much as 50% relative error. The 49.9% will become zero while 50.1% will become one, but each of the two numbers could get a different binary value. The binary scale has been widely used to characterize health conditions, disease definitions, blood pressure, selection of control groups, etc. The binary scale is not used in nature but is imposed by human will (e.g., normal blood pressure). The normal and abnormal marking system is widely used for chemical analysis data and thus introduces excessive errors relative to the required accuracy for correcting chronic diseases. Categories are also used in classifying side effects, cancer stages, etc. in an attempt to break continuous properties into categories by human wills. The binary scale does not provide the precision required to characterize chronic diseases.
“Medicine can cure diseases” is the oldest presumption that everyone takes it for granted. The first synthetic drug, chloral hydrate, was discovered in 1832 by Justus von Liebig in Gießen and introduced as a sedative-hypnotic in 1869 . Before the start of the new drug industry, all medicines, referred to in old medical literature, were natural products comprising a mixture of natural compounds, and most medicines are even formulations of natural products like herbs. After 1869, the medicine definition was changed without examining its validity to mean mainly active synthetic components . There are several important changes to the original meaning. Old medicines work like multiple-component diets with much milder effects while synthetic drugs are used at higher concentrations. Second, early medicines are things that once worked as a selection pressure in evolution. For example, the compounds from herbs, plants, and natural products might have found their way to human bodies through the food chain. It is reasonable to infer that the human body can tolerate them in low concentrations. The fecundity phenotype will not be passed on to the next generation if the person cannot tolerate natural compounds at low concentrations, and dies before reaching reproductive age. In comparison, most human beings are not exposed to synthetic drugs, and thus selection will start upon ingesting such compounds. Those two things affect drug side effects and the ability to restore the biological pathway networks.
The flaws in clinical trials and the failure of reductionist treatments are two different things but share some common elements. We have proved that treatments derived from clinical trials are deemed to be poor or inherently dangerous due to mismatched applications . Besides the mismatch of treatment, the poor performance of population-based treatments can be attributed to the reductionist approach, which is found to be poor or unworkable in nutrition [122,123], lower back pain , neuroscience and brain research [125,126], diagnostics , exercise , patient care [129-140], public health programs , and holistic medicine [110,133-135]. The evidence, taken as a whole, has firmly established that reductionist treatments are inferior. Those findings in combination with our simulation study  prove that reductionism is a wrong approach to chronic diseases.
Most medical treatments are developed according to reductionist thinking. The reductionist idea is that the human body is like a machine, and any fault can be fixed by targeting the fault part. This notion has been proved in some aspects such as organ transplant. However, we also see severe limitations. For example, after a person has died for some time, there is no way to revive the dead person like restarting a repaired car. The human ability to intervene brain is very limited. A reductionist treatment always has two components: a treatment is developed from a population and applied to the patient in the treatment of a disease. Both components are responsible for the failure of reductionist treatments.
A treatment derived from clinical trials is mismatched to patients. For example, John Doe suffers from Vitamin A deficiency but Jack Doe suffers from Vitamin A poisoning. Both are sick even though their average is perfect. If a treatment is developed from such a population, the treatment reflects an impermissible transfer of process attributes in the biological networks. The treatment cannot be valid for both of them. This is not an isolated problem but a universal problem in treatments for chronic diseases. Most, if not all, of nutrients, pollutants, activity levels, etc. are expected to have both positive and negative effects. If a treatment is used to affect one of such factors, the treatment must be improper for a considerable portion of persons. Even if a treatment has a constant treatment effect, the interfering factors affect the treatment (Tables 1-3). Mathematics makes an impermissible average in finding treatments and such treatments cannot be good for anyone except by accident.
Treatments derived from population trials always make improper trade harmful to patients. In a mini-trial comprising a 90-year-old man, a 40-year-old man, a 40-year-old female, and a 10-year-old boy, their health and disease properties must vary greatly. We acquire data and find an average value of a health property for this population. The data does not form a statistical population. If we impose the averaged value onto all of them by imagined measure, we should anticipate that the measure most probably will kill all of them in the long run. Obviously, to develop a treatment by the population approach, attempts are made to balance age effects and sex effects. Treatment of the old man is balanced by the need to offer benefits to the young boy. Treatment for the man is balanced by the need to offer benefits to the female. This mathematical averaging violates our observed principle that health property cannot be altered arbitrarily and cannot be transferred from one person to another person. Any treatment based on the representation principle must be detrimental to all persons if the treatment is used for a long term. This flaw cannot be cured by increasing the number of participants in the trial.
Even the responsive rate used in medicine is a poor concept. Two treatments with 5% curative rates are considered in mathematics as same. However, they mean completely different things if one treatment cures only females while another cures only males. The population model makes an assumption that all persons are treated in the same way, but in reality, they cannot. What is really important is who will survive and who will die. Treatments determined by using a mathematical model are insensitive to personal differences and cannot be used to formulate the best treatments for all persons. Two treatments respectively with responsive rates of 50% and 40% lack comparison basis and cannot be compared. If they work with entirely different persons, they would treat 90% of the population if they are matched to the right persons. If their joint responsive rate is 20%, they would benefit 70% of the population.
If a treatment is to alter a single process attribute in the biological network, such a treatment cannot correct all departures in the biological pathways network. One well-known example is the alteration of biochemical and cellular pathways in cancer patients: attributes of six categories of biological properties (growth signaling, cell apoptosis, anti-growth signaling, angiogenesis, tissue invasion and metastasis, and cell replication limits) are changed in cancer patients . Those process attributes are shown in P1, P2…. Pn in Figure 1. The top diagram shows a plurality of process attributes. Fault environmental, dietary, emotional, and lifestyle factors slowly cause many process attributes to depart from healthy values. It is highly unlikely that the disturbed biological networks can be corrected by using one single synthetic drug. This may be the reason why drugs deliver results that are poorer than what is predicted in theory. It is anticipated that the application of several primitive factors can have better chances to correct the departures in process attributes responsible for chronic diseases.
A treatment targeted to one attribute such as P2 of one pathway lacks sufficient driving force. Such treatment cannot correct all departures in process attributes but most probably disturbs other process attributes. If a treatment is to alter the rate of one biological pathway, it is impossible to tell how the treatment might alter other pathways. Besides, responses in other pathways might depend on personal variations. Thus, one should find that diseases cannot be cured by correcting one seemingly faulty pathway. Some unexpected changes in other process attributes may make personal health or disease worse. This is one reason that synthetic drugs fail to work.
Figure 2 may be used to interpret the cancer treatment model. In this case, P1 to Pn could mean, respectively, genetic mutations, foreign matter, microbiota, pollutants, and toxins, nutrition, heavy metals, vascular condition, organ functional capacities, inflammation biomarkers, micro-circulation condition, innate immunity, CNS condition, nerve health, glucose level, emotional states, etc. Microbiota affects metabolic byproducts which may damage cells; foreign matter such as fiber grass and asbestos may affect tissue’s local environment. P1 to Pn may denote process attributes (e.g., the glucose level) or anything that can directly or indirectly affect the process attributes of local biological pathway networks. Each of the P1 to Pn bars may mean a large number of primitive factors. Foreign matter may mean anything that could disturb the biological pathways with an effect to promote cancer. Toxins may mean one or more of potentially thousands of known and unknown compounds. With the massive variations in mind, Figure 1 may represent completely different process attributes for the same disease for two different persons. Two colon cancer patients may have completely different process attributes in Figure 1 and naturally require different lifestyle prescriptions. Considering their lifestyles, one may be caused by using a large amount of salty, hot, fried snacks while the other might be caused by excessive stress, distinctive microbiota, and lack of exercise. While they may share some common things, they must be treated differently to achieve better results.
A mathematical model developed for a person cannot be used to predict disease severity for other persons unless they are close to the person. The model may tell how to change dependent variables to achieve a better outcome. We must find that current epidemiological models [141-150] are irrelevant to the disease and human health. An articulation like “temperature or oxygen can cure the disease” has far more science than a mathematical model. Epidemiological models contain almost none of the biological factors. Manipulation of the twenties to a hundred factors, most of which are conceived out of imagination, will not help solve the pandemic. Medicine needs to pay more attention to human disease biology and personal health, rather than things like coins, dice, lotteries, etc. It is a wrong strategy to jump onto population health while forgetting basic disease biology.
Strong treatment that is not matched to the patient’s condition must produce drug side effects if the treatment is used for a long time [137-140,151-155].
When medicine evolved from experienced-based ancient mind-body personalized medicine to drug-based reductionist modern medicine, medicine silently changed multi-component natural mild medicines into highly concentrated synthetic drugs, changed the holistic mind-body medicine into reductionist treatments, turned health properties of human beings into statistical populations, and started using mathematical models for most research subjects. Those four presumptions have been accepted as the foundation of medicine, without ever being validated. Our analysis using massive data produced by tens of thousands of medical studies firmly refutes the presumptions. The biggest flaw in modern medicine is the use of mathematical models to add up process attributes, which are intensive properties, across individual persons. While this presumption is not always wrong for all research purposes, it is always wrong in a trial to study chronic diseases. Chronic diseases are a manifestation of distinctive biological pathway networks in humans often with very small departures in multiple process attributes, the addition of intensive properties across different persons produces meaningless results except for a strong treatment. Without overhauling the foundation, modern medicine lacks the required accuracy for accurately characterizing chronic diseases and will never find predictable cures for chronic diseases.
In assessing treatment effect, (1) a clinical trial is useful in a study when a strong treatment T is against N interfering factors Fi, where T is much and much stronger than the sum of all interfering factors Fi. In this situation, the statistical population exits by approximation while all interfering factors can be viewed as experimental errors, and if the treatment is not intended to disturb the biological networks, the treatment is only for short-term use; (2) clinical trial may be able to detect the effect of treatment T among N interfering factors Fi if the accumulated effect of the treatment in a sufficiently long period can stand out accumulated interfering factors Fi. This is why some long-term trials can result in useful estimates; (3) clinical trials cannot produce the right result if a treatment T in the study is used only for a brief time so that its treatment effects are hidden among N interfering factors Fi. A brief exercise, brief diet, and brief emotional invention show no detectable benefit; and (4) a clinical trial produces a false result if treatment T in a study does not have the same or similar effects on all subjects, or if all interfering factors have different impacts on different subjects, or both. In those situations, the positive effects of the treatment T on some subjects are nullified by negative effects on other subjects, or distorted by randomly interfering factors. Moreover, treatment effects on all subjects must be compared with differences attributable to different subjects in light of the accuracy requirement of investigation purposes.
Clinical trials are invalid for studying drug side effects that are realized slowly while the measured health properties are interfered with by a large number of interfering factors. Among the massive medical studies, only one class of studies directed to strong treatment effects is correct by approximation as far as treatment effects are concerned. However, those studies may still fail to produce correct results for the side effects of treatments.
Root flaws in clinical trials include (1) misuse of statistical populations as presumption without considering research purposes and required accuracy; (2) failure to study the massive interfering factors and co-causal factors; (3) misuse of mathematical models with attempts to change process attributes as if they were fungible properties; (4) use of a mathematical model that is remote from reality; (5) misuse of representative principle when there is no statistical population and no approximation is allowed; (6) failure to determine required accuracy relative to interfering factors; and (7) failure to address one-way biases caused by interfering factors. Each of those problems can make study findings inaccurate, biased, or even completely meaningless.
Due to the widespread use of clinical trials, all studies involving weak environmental, dietary, emotional, and lifestyle factors are most probably wrong if the findings are negative. This flawed method in the studies poses the worst risk to civilization. Each of the potentially thousands of harmful and toxic substances is found to be harmless because it is interfered with by a large number of other interfering factors. Those harmful substances strike human health slowly. We regard the misused clinical trials as well as population-based studies as the main reasons for human inability to protect the ecosystem and provide lame excuses for the abusive use of avoidable toxic substances such as food additives, synthetic flavors, texture modifiers, etc. Now, cancer, infertility, mental diseases, infectious diseases, etc. are striking mankind with unprecedented impacts. The risk of getting cancer in a person’s lifetime rises from 0.04 to 0.4 and will soon reach unity. Stories like six cancer patients in a family and three cancer deaths in a year will become more frequent. There is no way to find causes, and nothing could be attempted to arrest many bad trends. Problems like infertility will become a civilization crisis.
Reductionist treatments cannot cure chronic diseases for all of the following reasons: The treatment (1) is based on clinical trials that attempt to make impermissible trades in health properties between different persons; (2) is mismatched to individual persons’ biological networks; (3) fails to address the interlocking role of CNS; (4) lacks the force to alter departures of process attributes in the biological networks; (5) cannot deal with multiple layers of mechanisms concerning disease initiation, progression, innate immune response, adaptive immune response, resolution of inflammation, and resolution of damages; and (6) has a too strong force to distort intervened biological networks and thus causes drug side effects by further distorting the biological networks. This proves why medicine has failed for centuries.
Population-derived treatments should be presumed to be dangerous if they are used to directly affect a step in the biological pathways networks for the long term. Biological pathways in each person must be maintained as a distinctive pattern in terms of intermediate species concentrations and conversion rates. Each person’s values cannot be changed into the population averages or the values of another person because the person’s values are constrained by upper-stream, coupled, and downstream biological pathways in the pathways networks. It is impossible to alter just one single or a few steps in the networks without altering others. Intervention by using a strong drug must distort other process attributes and thus cause new diseases. This distinctive nature implies that the mathematical averages of the values have no meaning and that chronic diseases cannot be cured by intervening in one single or a few steps in the networks.
Immediate cures for chronic diseases lie in the use of primitive factors to slowly alter personal biological networks. The use of evolution-compatible lifestyle factors is presumed to be the safest cure. Most biological properties (except genes and age) can be altered by using lifestyles, foods, exercise, emotion management, and avoidance of toxic substances. Even certain genetic properties can be altered to a limited extent by changing epigenetics. The effects of many primitive factors have been found in studies; and the effects of weak factors such as activity habits, cellular phone use, etc. cannot be determined under current research models but may be established by studying disease mechanisms or disease risks. The adverse effects of lack of exercise and inactivity can be seen in elevated disease risks among obese people, people having vascular diseases, and people who are sedentary. However, negative findings in clinical trials should not be read to preclude the potential benefits of weak factors on specific persons.
Most diseases progress from altering primitive factors, altering the biological networks, altering the structure, and altering the CNS function. The severity of chronic diseases is almost always manifested in functional impairment caused by structural changes. Changes in each of those phases may take considerable time, and disease development and disease reversal would take considerable time. Preventive measures may be used in any of those phases, and there is no difference in mechanism between curative measures and preventive measures except that reversing diseases is more difficult.
The democratic representation principle used in research harms minorities. Treatments developed in clinical trials can have severe adverse impacts on minorities. Genetics are more similar between persons within a race than between members of different races. The computed average of health property for a population of a majority race is expected to be different from that for a population of a minority race. If a treatment is developed by a clinical trial containing persons of both races according to their population ratio, the average will be closer to the average of the majority race. This implies that the treatment for the minority race is poorer or more dangerous than it is for the majority race.
The problems in clinical trials are found by studying its theory, basic assumptions, biological pathways, disease initiation and defense mechanisms, factor-factor interactions, body structure, etc. Treatments developed by using the population approach are inherently invalid except by accident and inherently dangerous if they are used in the long term. All four presumptions -- treating health property as a population, using synthetic drugs, using mathematical and statistical methods, and using reductionist treatments -- are refuted. The flawed foundation precludes finding predictable cures for chronic diseases and cancer.
The findings of this study will affect the lives of tens of millions of people who are dying from chronic diseases in the world each year, the use of a good part of $41 billion in federal research funds administered by NIH, the use of more than a hundred billion private research fund in the U.S., the trillion of medical spending in the U.S., validity and accuracy of conclusions in a large number of published medical studies, and risk to civilization. The worst effect of those flaws is that it creates incurable notion to justify the failure of medicine. By trusting the flawed research model and treatments, medicine fails to study hundreds of well-documented cancer miracles and does not attempt to discover millions of undocumented miracles in cancer, heart diseases, and mental diseases. The ruined health wisdom among patients suffering “terminal” diseases is a strong aggravating factor that causes patient deaths because patients are misled by propagation that diseases are incurable. If one truly understands all flawed presumptions and all flaws in research and treatment models, one should see that diseases are curable among patients with the willpower to change. Unfortunately, the medical establishment has built highly sophisticated protective systems to discriminate against, preclude, and suppress any research findings that challenge the foundation of medicine.
The author(s) declared that no grant was used in support of this research project.
Both authors contributed to the research and development of the analysis. However, the final edits were made by Jianqing Wu only.
Due to the scope of the work, additional information will be provided in follow-up studies which will be posted in Preprint servers.
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