Similar postprandial glycemic reductions with escalation of dose and administration time of American ginseng in type 2 diabetes.
Bibliographic record
Abstract
OBJECTIVE: We previously demonstrated that 3 g American ginseng (AG) reduced postprandial glycemia (PPG) in type 2 diabetic individuals. We investigated whether further reductions can be achieved with escalation of dose and time of AG administration. RESEARCH DESIGN AND METHODS: Ten type 2 diabetic patients (6 men, 4 women; age 63+/-2 years; BMI 27.7+/-1.5 kg/m2; HbA1c 7.3+/-0.3%) were randomly administered 0 g (placebo) or 3, 6, or 9 g ground AG root in capsules at 120, 80, 40, or 0 min before a 25-g oral glucose challenge. Capillary blood glucose was measured before ingestion of AG or placebo and at 0, 15, 30, 45, 60, 90, and 120 min from the start of the glucose challenge. RESULTS: Two-way analysis of variance (ANOVA) demonstrated that treatment (0, 3, 6, and 9 g AG) but not time of administration (120, 80, 40, or 0 min before the challenge) significantly affected PPG (P<0.05), with significant (P = 0.037) interaction for area under the curve (AUC). Pairwise comparisons showed that compared with 0 g (placebo), 3, 6, or 9 g significantly (P<0.05) reduced AUC (19.7, 15.3, and 15.9%, respectively) and incremental glycemia at 30 min (16.3, 18.4, and 18.4%, respectively), 45 min (12.5, 14.3, and 14.3%, respectively), and 120 min (59.1, 40.9, and 45.5%, respectively). However, pairwise comparisons showed no differences between the 3-, 6-, or 9-g doses and any of the times of administration. CONCLUSIONS: AG reduced PPG irrespective of dose and time of administration. No more than 3 g AG was required at any time in relation to the challenge to achieve reductions. Because these reductions included glycemia at the 2-h diagnostic end point, there may be implications for diabetes diagnosis and treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".