Bibliographic record
Abstract
Les pathologies rénales sont actuellement en constante augmentation dans les sociétés occidentales du fait des conditions de vie d’un individu (âge, mode de vie, pathologie chronique, etc.) ou encore de l’exposition potentielle à de nombreuses substances néphrotoxiques. Aujourd’hui, les mécanismes d’atteinte rénale sont de mieux en mieux connus. Néanmoins, le rein étant une structure complexe et multifonctionnelle, la gestion clinique des déficiences rénales (pronostic, diagnostic, mise en place d’une thérapie) reste toujours un problème significatif en clinique. En effet, les paramètres cliniques traditionnels sont peu sensibles et non discriminants et indiquent plutôt une déficience fonctionnelle qu’une altération tissulaire sous-jacente. Dans ce contexte, l’identification et le développement de nouveaux biomarqueurs d’atteinte rénale entrepris depuis quelques années devraient permettre une identification de manière plus précoce, plus spécifique et plus sensible des insuffisances rénales aiguës ou chroniques en clinique et ainsi une meilleure appréhension de la prise en charge clinique de ces déficiences rénales. Over the last few decades, prevalence of renal diseases has grown continuously in occidental societies due to life conditions (age, life style, chronic disease, etc.) or potential exposure to nephrotoxic agents (drugs and environmental chemicals). Today, the knowledge of the nephropatology mechanism is improving. Nevertheless, considering it is a complex and multifunctional structure, the clinical strategy of this issue (prognostic, diagnostic or therapy) keeps posing a major challenge for clinicians mostly because classical markers are not sensitive enough and require hours before reaching significant levels. Furthermore, most of these markers provide information on function and not on structural integrity of the tissue. Identification and development of new biomarkers share promise of improvement in the rapid diagnostic of kidney diseases and development of new cures in order to optimize the clinical strategy associated to the renal failure.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".