The Impact of Withholding Cost-Effective Early Treatments, such as Vitamin D, on COVID-19: An Analysis Using an Innovative Logical ParadigmThe Impact of Withholding Cost-Effective Early Treatments, such as Vitamin D, on COVID-19: An Analysis Using an Inno

Abstract

Purpose: While numerous widely accepted nutraceuticals lack randomized clinical trial (RCT) validation, regulatory bodies prioritize RCTs as the primary evidence for testing hypotheses for drug approvals. Despite challenges in authorizing generic therapies for SARS-CoV-2, regulatory bodies promptly granted Emergency Use Authorization for patented agents. This study evaluated whether the clinical trial data yielded adequate evidence to justify the approval of generic compounds, like vitamin D, as adjunct therapy to combat SARS-CoV-2. Methods: We employed an ancient logic system, seamlessly integrated with modern scientific principles and artificial intelligence principles, to analyze empirical data from 7 papers published in 2020. Subsequently, we compared the results with over 200 scientific papers (including over 100 treatment studies) referenced in a large (big)database. This study aimed to determine if there was substantial evidence in 2020 to support the approval of generic agents such as vitamin D (and ivermectin) for treating COVID-19. Results: The drug approval process undervalues well-designed observational studies, placing them in a subordinate position to RCTs when assessing effectiveness, even for nutrients. Our utilization of Catuskoti, an innovative logical method, highlights its potential as a catalyst for scientific progress, including big data analysis and integrating artificial intelligence into nutrient and pharmaceutical approval processes. Conclusions: Analyses conducted within this logical framework affirmed a robust inverse correlation between vitamin D levels and positive clinical outcomes in COVID-19 cases. Emphasizing the broader adoption of Catuskoti logic, particularly in analyzing big data and Machine Learning paradigms, becomes crucial in drug approvals. This approach aims to mitigate harm to individuals in future pandemics by promptly providing a more comprehensive understanding of the relationships between variables.