Est-il possible de décider d’un ordre de priorité dans nos investissements dans les technologies médicales et autres programmes de santé?
Bibliographic record
Abstract
Dans le domaine des soins médicaux, l'adoption rapide de nouvelles méthodes thérapeutiques semble s'imposer. Dans cet article, l'auteur analyse le cas précis des nouvelles technologies de la santé afin de voir de quelle façon les sociétés contemporaines arrêtent leur choix en matière d'investissements porteurs de santé. Il examine les méthodes officielles d'évaluation des technologies en vigueur aujourd'hui et présente deux études de cas qui illustrent la difficulté de faire des choix rationnels dans ce domaine. En conclusion, l'auteur prône une évaluation des interventions sociales et économiques en fonction de leur influence sur la santé et soutient que le moyen le plus efficace de réduire la demande des seules technologies médicales à l'égard des ressources que la société consacre à la santé serait peut-être d'accroître la concurrence face à ces ressources.
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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 | 0.098 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".