Le rapport cholestérol total/HDL cholestérol prédit mieux le risque cardiovasculaire que le rapport LDL cholestérol/HDL cholestérol
Bibliographic record
Abstract
Les rapports cholesterol total (CT)/ HDL-C et LDL-C/HDL-C sont utilises frequemment pour evaluer le risque de maladie coronarienne. Il n'y a toutefois pas de consensus sur la superiorite de l'un ou l'autre de ces deux indices. Dans une etude quebecoise (Quebec cardiovascular study), l'equipe de Jean-Pierre Despres a mesure ces deux rapports chez 2103 hommes d'âge moyen, recrutes dans la region de Quebec. Pour chaque rapport LDL-C/HDL-C, le rapport CT/HDL-C etait superieur chez les hommes ayant des triglycerides dans le tertile superieur (> 1,68 g/l, 1,9 mmol/l) par rapport aux 1er et 2eme tertiles. L'ajustement du rapport CT/HDL-C pour le rapport LDL-C/HDL-C par analyse de covariance a genere des differences significatives dans les rapports CT/HDL-C en fonction des tertiles de triglycerides. La comparaison des tertiles du CT/ HDL-C donnait plus de renseignements que celle du rapport LDL-C/HDL-C sur les differences concernant le syndrome d'insulinoresistance (insulinemie, apoB et taille des LDL). Les variations du rapport CT/HDL-C semblent donc associees a des modifications plus nettes des indices metaboliques predictifs d'un risque ischemique coronarien lie au syndrome d'insulinoresistance. Lemieux I., et al. 2001. Total cholesterol/HDL cholesterol ratio vs LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men. Arch Intern Med 161 : 2685-2692.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".