Bibliographic record
Abstract
This Humanities in Medicine article is an examination of the use of formal fine arts training in medical curricula to enhance diagnostic skills. A great amount can be discerned about pathology and pathophysiology using visual cues. Conventional medical education stresses the importance of physical diagnostic skills but often omits explicit teaching on how to methodically observe for information that could be useful for diagnosis. The current curriculum could be greatly complimented by the study of fine arts, which deals directly with the careful observation, description, and interpretation of the visual world. Cet article sur la médecine et les humanités est un aperçu sur la pertinence d’incorporer une formation formelle des beaux-arts dans le curriculum médical afin d’optimiser l’habileté des cliniciens à poser un bon diagnostic. L’utilisation de repères visuels est d’une grande utilité pour discerner la pathologie et la physiopathologie de différentes maladies. L’éducation médicale conventionnelle souligne l’importance de l’examen physique lorsqu’on doit poser un diagnostic, mais néglige parfois l’enseignement d’une approche méthodique qui utilise activement l’observation afin de repérer des informations qui pourraient être très utiles dans le diagnostic d’un patient. Le curriculum actuel pourrait très bien incorporer l’étude des beaux-arts, car celle-ci implique une observation, une description et une interprétation du monde visuel qui nous entoure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".