Bibliographic record
Abstract
La prise en charge des patients atteints d’insuffisance rénale chronique présente en France de graves difficultés : insuffisance de la prévention, inégalités d’accès à la greffe, développement insuffisant des modalités de dialyse autonome. Les explications sont multifactorielles, mais les procédures de rémunération des acteurs et des structures de soins entrent en jeu en partie. Pour résoudre ces problèmes liés au mode de financement, des réflexions sont menées depuis quelques années sur la mise en place d’une tarification du parcours de soins, intégrant forfaitairement les soins et prestations directement liés à la prise en charge. Cette évolution du financement présente toutefois des limites et des risques pour la qualité des soins, nécessitant que soient mis en place un cadre précis en termes de référentiel de pratiques et d’indicateurs de qualité, un système d’information et d’évaluation performant, une organisation plus intégrée des soins. La France doit, dans cet esprit, expérimenter en 2019 certains modèles de tarification selon le parcours. In France, serious difficulties exist in the management of patients suffering from chronic kidney disease: insufficient prevention, inequalities in access to transplantation, insufficient development of autonomous dialysis. The explanations are multifactorial but partly involve the procedures for remunerating professionals and health care facilities. To solve these problems related to the financing method, discussions have been underway for several years on the implementation of bundle payment, including all care and services. However, this evolution presents limits and risks for the quality of care, requiring the implementation of a precise framework in terms of guidelines and quality indicators, an efficient information and evaluation system, and a more integrated organization of care. During 2019, it is planned to carry out experiments on certain models of bundle payments for chronic kidney disease.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".