<b>Clinical Trials and Reductionist Approach Preclude Cures for Chronic Diseases </b><b>Due to Flawed </b><b>Presumptions</b>

Abstract

Modern 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 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 manifestation of small departures in multiple process attributes in distinctive personal biological pathways networks, that modern medicine lacks 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 the 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.

Immune Functional Capability Dynamics Determine Disease Outcomes as in Cancer, Heart Diseases, COVID-19, Etc.

“…All debates on vaccine benefits and disease risks cannot be resolved without understanding the deep flaws in population-based approach, statistical model, and side-effect evaluation model [1][2]. The population approach, if used to evaluate weak factors, is wrong because it assumes that health property can be studied by using a statistical distribution, which did not exist [2]. All population studies also violate the rule against using intensive properties OUTSIDE each system.…”

“…Expected side-effect of drugs, which are used to treat the infection, may also weaken immune system's current functional capacity. While the effects of second infection on a primary infection cannot be detected in clinical trials [1][2], they could be proved by theoretical approach [187]. Influenza infection is found to enhance the production of the anti-inflammatory cytokine interleukin (IL) 10 (IL-10), thereby suppressing the overall immune response and increasing susceptibility to secondary infections [219].…”

“…In our second study [2], we deeply examined the presumptions that are used in the medical research model and we found that all key presumptions do not hold. We further found that statistical observations must be values representing a property that is fully defined and cannot be changed during a trial.…”

“…To further explore the flaws in clinical trials, we examined four presumptions, and found, among others, that statistical population of health properties simply does not exist for most research purposes, mathematical models are misused to model intensive properties, and reductionist treatments are inferior and inherently dangerous [2].…”

Challenge to Vaccine Side Effect Evaluation and Reporting Models:A New Classification Methods

“…Our warning is based on all-time failure of clinical trials in determining drugs side effects [1][2]. It is no brain to conclude that clinical trials are a wrong research model.…”