Adaptive many model medicine trumps monocultural models: comment on the Miles and Mezzich emergent model of modern clinical practice
Bibliographic record
Abstract
In their provocative and insightful discussion paper, Miles and Mezzich consider two parallel, but philosophically divergent movements in medicine: evidence-based medicine and patient-centered care. They call for the integration or coalescence of these contrasting movements into one model that "combines the strengths of both movements, but which dispenses with the weaknesses of each." I share their goal of placing the person at the center of medicine, rather than subordinating the person to the depersonalized science and technology represented by current models of evidence-based medicine. Yet I envision a person-centered model, indeed any medical model, not as an overriding unified entity, but rather as one component in a complex "many model medicine". I have tried to show elsewhere that the use of many models is likely to produce better outcomes than the dominance of any single model. Multiple models entail multiple perspectives and methods that may be necessary to solve difficult medical problems. This pluralistic view is consistent with Peabody's view, cited in the discussion paper, that medical art and science are not opposites, but are foundational components of medicine.
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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.033 | 0.064 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".