L'évaluation des capacités de la conduite automobile pour les personnes atteintes de lésion cérébrale acquise non évolutive: enfin une recommandation de bonne pratique labellisée par la HAS; quel impact sur la pratique ergothérapique ?
Bibliographic record
Abstract
En France comme a l’etranger, les ergotherapeutes sont fortement impliques dans l’evaluation des capacites de conduite automobile. Si des « guidelines » existent en Australie (NTC, 2012), aux Etats-Unis et au Canada (CCMTA, 2013) depuis plusieurs annees, en France l’arrete du 18 decembre 2015 qui dresse la liste des affections medicales incompatibles avec l’obtention ou le maintien du permis de conduire comporte des limites. En effet, il ne decrit pas precisement les moyens et modalites d’evaluation a mettre en oeuvre, particulierement pour l’evaluation des troubles cognitifs et comportementaux des personnes atteintes de lesion cerebrale. En janvier 2016 : la recommandation de bonne pratique sur la reprise de la conduite apres une Lesion cerebrale acquise non evolutive (LCANE), labellisee par la HAS, correspond a ce « guideline » tant attendu. Cet article abordera les principaux points des recommandations qui peuvent influencer la pratique et le discours des ergotherapeutes aupres des personnes presentant une LCANE.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".