Organisation des soins spécialisés et intégrés en ambulatoire pour la prise en charge de la maladie rénale chronique en pédiatrie : expérience du CHU Sainte-Justine
Bibliographic record
Abstract
La prise en charge des enfants atteints de maladies rénales chroniques est basée sur la mise en place de soins complexes qui ont pour but d’améliorer la croissance, le développement et ultimement la qualité de vie des enfants. Les cliniques externes traditionnelles sont souvent insuffisantes et inadéquates pour répondre aux spécificités pédiatriques de la maladie. Récemment, les cliniques spécialisées de maladie rénale chronique et de prédialyse se sont développées pour faciliter et améliorer la prise en charge des patients avec une approche globale et multidisciplinaire. Nous rapportons ici l’expérience pédiatrique de ce mode de suivi avec l’organisation et la mise en place d’une clinique spécialisée prévention optimale de l’insuffisance rénale (Prévoir), au niveau du CHU de Sainte-Justine. The management and optimal care for the pediatric patient with chronic kidney disease requires attention not only to medical management, but also special focus on the psychosocial and developmental factors of children which is complicated by the presence of other disease-related complications. In recent years, specialized chronic kidney disease and predialysis clinics have been set up to facilitate and improve the quality of care of these patients with a multidisciplinary organisation and coordinated management approaches of a renal team. We present our experience in establishing such a renal management clinic named “Prévoir” for children with chronic kidney disease at Sainte-Justine Hospital.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".