Bibliographic record
Abstract
Many medicines have been proposed to cure various ills of biomedicine including evidence-based medicine, evolutionary medicine, narrative medicine, and complexity medicine, among others. To the extent that all models are idealizations or abstractions, all of these model medicines are imperfect in some respects. In the absence of a single unified model, if indeed unification is possible or even desirable, and despite the relative advantages of one model or another, in practice many models and methods are necessary in medicine. In this article, I consider the value of such diversity in models and methods. I briefly describe several models. Then I discuss simulations of agents who use diverse models. Advocates of models such as those discussed here typically claim that we should use their preferred model because it is the best. Evidence-based medicine, for instance, has been promoted as the single best model of medicine while other models have been cast as lesser models or in opposition to it and each other. But isolated models and methods may never be as good as groups of models and methods. Debates about various individual models may result in better outcomes, but explicitly choosing to use many models is likely to produce even better outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".