Bibliographic record
Abstract
L'utilisation des plantes medicinales a considerablement augmente ces dernieres annees, en l'absence d'arguments medicaux. Le ginseng est une des plantes les plus largement utilisees. Il est repute jouer un role dans le metabo-lisme glucidique. Une equipe canadienne a evalue a court terme, si le ginseng americain (panax quinquefolius L) modifiait la glycemie post-prandiale. Dix sujets non diabetiques et 9 presentant un diabete de type 2 ont ete randomises pour recevoir 3 g de ginseng ou du placebo, soit 40 minutes avant, soit en meme temps qu'une charge orale en glucose de 25 g. Chez les sujets non diabetiques, la glycemie post-prandiale etait identique apres prise du placebo ou du ginseng lorsque les 2 capsules etaient administrees en meme temps que la charge en glucose. En revanche, lorsque le gin-seng etait pris 40 minutes avant lacharge en glucose, une reduction significative de la glycemie etait observee. Des resultats comparables ont ete trouves chez les diabetiques de type 2 (figure). La reduction de la glycemie (evaluee en aire sous la courbe ASC) etait de 18 % ± 31 % chez les non diabetiques et de 19 ± 22 % chez les diabetiques de type 2. Le ginseng americain diminue donc la glycemie post-prandiale chez les non-diabetiques et chez les diabetiques, a condition de le prendre 1/2 heure avant le repas. Vuksan V., et al. 2000. American ginseng (panax quinquefolius L) reduces postprandial glycemia in nondiabetic subjects and subjects with type 2 diabetes mellitus. Arch Intern Med 160 : 1009-1013
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".