Le processus d’attribution rénale québécois défavorise-t-il les patients en attente d’une greffe combinée rein-pancréas?
Bibliographic record
Abstract
En transplantation d'organes, il y a un écart important entre le nombre d'organes disponibles et le nombre de patients en attente.Afin de mieux répartir ces organes, les règles d'allocation s'appuient sur des principes d'équité et d'utilité médicale.Dans cette étude de cas, nous allons illustrer la difficulté associée à la mise en place d'un système d'allocation.Celui-ci a pour objectif de garantir une répartition équitable des greffons rénaux tout en tenant compte du principe d'utilité et ce, pour tous les patients en attente, dont les patients en attente d'une greffe combinée rein-pancréas.In organ transplantation, there is a significant gap between the number of organs available and the number of patients waiting.To better distribute these organs, allocation rules are based on principles of equity and medical utility.In this case study, we will illustrate the difficulties associated with the implementation of an allocation system.The aim is to ensure a fair distribution of kidney transplants while also taking into account the principle of utility, and this for all patients, including those waiting for a combined kidneypancreas transplant.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".