Financement des blocs opératoires en France et en Belgique, nomenclatures belge et française
Bibliographic record
Abstract
Objectives: This study aims to compare the calculation of the grants made by the Belgian and French authorities to assume the financing of the nurses working in operating theatres. The simultaneous use of two nomenclatures (Belgian "INAMI" and French "CCAM") gave rise to questions about the possibilities of automatic transcribing between these two catalogues of medical acts. Method: All the interventions of the first quarter 2002 of a Belgian operating theatre were subject to a double encoding ("INAMI" and "CCAM" codes) according to operational protocols. The standard times used as part of the Belgian hospital financing were compared with the real times and with the operational times used as part of the French ICR system. Results: The standard times allocated in Belgium for the financing of nurses working in operating theatres are better correlated with real time than those employed within the framework of the ICR which were used during the study. The higher degree of accuracy of the CCAM makes the automatic transcribing of Belgian medical acts into French medical codes uncertain. Conclusion: This study substantiates the idea that it would be interesting to carry out a validation of ICR scales on the basis of real data.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".