Traduction et adaptation française de la nomenclature pour la fonction et les maladies rénales issue de la conférence de consensus KDIGO
Bibliographic record
Abstract
A harmonized medical nomenclature that is accessible to the lay people is crucial to raising awareness of insidious health problems such as chronic kidney disease and facilitating communication between healthcare professionals. This article presents the proposals of a French-speaking working group for the translation and adaptation into French of the nomenclature for renal function and disease that resulted from a KDIGO consensus conference published in English in 2020. In particular, the working group recommends abandoning terms that used to correspond in French to “chronic renal failure”, “acute renal failure”, “end-stage renal failure”, “uremia”, “cadaveric donor” and “microalbuminuria”, in favor of French equivalents of “chronic renal disease”, “acute kidney injury”, “renal failure”, “uremic syndrome”, “deceased donor” and “albuminuria”. Arguments against the former and in favor of the latter are presented. Other equivalents of English terms from the KDIGO nomenclature are presented in a Table, and an Appendix presents equivalents proposed in German and Spanish by other authors. We hope that our proposals will be well received by healthcare professionals as well as by their patients and the public.
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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 | 0.051 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".