Quest for COVID-19 cure: integrating traditional herbal medicines in the modern drug paradigm
Bibliographic record
Abstract
The world is facing one of the biggest public health tragedies of our time, both in terms of socio-economic loss and death tolls due to the coronavirus COVID-19 pandemic. In a frantic race to find treatment for COVID-19, many interventions to discover drugs and vaccines are being expedited. Similarly, traditional herbal medicines are also being explored to find a cure for COVID-19. There are many traditional medicines that have exhibited promising antiviral and immuno-modulating properties against a plethora of infectious diseases like influenza, malaria, tuberculosis, and even COVID-19. Traditional medicine is an integral part of culture and practices in many countries with a vast and rich history of treating diseases. However, scientific research-based drug development approaches and effective regulatory mechanisms, on par with modern medicine, should be implemented to ensure safety, efficacy and overall validity of traditional medicine. Incorporating evidence-based traditional medicines in modern drug development paradigms can help assure affordability, accessibility and acceptability of the treatment approach. Furthermore, it can create pharmacological synergism to tackle drug resistance. Altogether, every country should create a roadmap for modernization and revival of traditional knowledge to improve the health care system and be better prepared for health crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".