Towards a transactional medicine approach to combating global emerging pathogens: the case of COVID-19
Bibliographic record
Abstract
When the COVID-19 pandemic struck and China reported the first case to the World Health Organization in December 2019, there was no evidence-based treatment to combat it. With the catastrophic situation that followed, materialised by a considerable number of deaths, researchers, doctors, traditional healers, and governments of all nations committed themselves to find therapeutic solutions, including preventive and curative. There are effective treatments offered both by modern medicine and traditional medicine for COVID-19 today. However, other therapeutic proposals have not been approved due to the lack of effectiveness and scientific rigour during their development process. Proponents of modern medicine prefer biomedical therapies while in some countries, traditional treatments are used regularly because of their availability, affordability and satisfaction they bring to the population. In this paper, we propose a transactional medicine approach where the interaction between traditional and modern medicine produces a change. With this approach, the promoters of traditional medicine and those of modern medicine will be able to acquire knowledge through the experience produced by their encounters. Transactional medicine aims to be a model for decolonising medicine and recognising the value of both traditional and modern medicine in the fight against COVID-19 and other global emerging pathogens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".