Les conceptions présidant à l’organisation du prélèvement d’organes et de la greffe en France, au Canada et aux États-Unis
Bibliographic record
Abstract
Le séminaire qui a eu lieu à l’Académie de médecine les 15 et 16 avril 2010 avait pour objectif d’éclaircir les raisons qui conduisent, de part et d’autre de l’Atlantique, malgré des valeurs dont les origines sont communes, à des choix différents sur les règles en matière d’anonymat, de don du vivant, de consentement pour le don d’organes après décès, de donneur à cœur arrêté, de donneur altruiste, de place des représentants des patients dans le système de soins. Le séminaire visait également à permettre aux députés et sénateurs qui préparent la loi de bioéthique d’avoir une mise en perspective internationale sur les sujets concernant la transplantation. The goal of the seminar which took place in Paris on April 15th and 16th, 2010, was to understand the reasons which lead, on both side of the Atlantic and from a common basis of values, to different choice about essential rules concerning: consent strategies for after death organ donation: non heart beating donation; anonymity; living donation; paired-exchange donation (“crossed donation”); altruistic donation (“good Samaritan”); donation incentives; the place devoted to patient's representatives in health systems. The seminar also aimed at providing the French legislator, while the law on bioethics was being discussed, with international perspective and useful information regarding ethical practices and reflections on these issues.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.044 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| 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".