La compensation financière des victimes d’accidents transfusionnels
Bibliographic record
Abstract
PRÉFACE 1 -- MESSAGES CLÉS 2 -- SYNTHÈSE 4 -- 1. CONTEXTE ET OBJECTIFS DE CE RAPPORT 5 -- 1.1. LE PRÉCÉDENT RAPPORT DU KCE (2010) 7 -- 1.2. LES OBJECTIFS DE CE RAPPORT 7 -- 1.3. MÉTHODE 8 -- 2. LES DISCRIMINATIONS POTENTIELLES 8 -- 2.1. DISCRIMINATION À L’ÉGARD DU TYPE DE PROBLÈME MÉDICAL PRIS EN CHARGE 8 -- 2.2. DISCRIMINATION PAR RAPPORT AU MOMENT DE LA CONTAMINATION 9 -- 2.3. DISCRIMINATION À L’ÉGARD DE LA VICTIME À INDEMNISER 10 -- 2.4. DISCRIMINATION PAR RAPPORT AU MODE DE COMPENSATION FINANCIÈRE 12 -- 2.5. NE PAS CRÉER UNE NOUVELLE DISCRIMINATION PAR MANQUE D’INFORMATION 12 -- 3. QUELQUES EXEMPLES ÉTRANGERS 13 -- 3.1. EN FRANCE 13 -- 3.2. EN SUÈDE 13 -- 3.3. EN ITALIE 13 -- 3.4. AU QUÉBEC 14 -- 3.5. EN ONTARIO 14 -- 4. VERS UN ÉLARGISSEMENT DES MISSIONS DU FAM 15 -- 5. LE COÛT DE LA NOUVELLE LOI 16 -- 5.1. PRÉAMBULE 16 -- 5.2. CHARGE FINANCIÈRE GLOBALE 17 -- RECOMMANDATIONS 18
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".