Pre‐prandial vs. post‐prandial capillary glucose measurements as targets for repaglinide dose titration in people with diet‐treated or metformin‐treated Type 2 diabetes: a randomized controlled clinical trial
Bibliographic record
Abstract
OBJECTIVE: Repaglinide is an oral anti-diabetic agent that has a short duration of action, and is suitable for preventing post-prandial rises in glucose levels. Targeting post-prandial glucose levels may lead to lower HbA(1c) levels and rates of hypoglycaemia than targeting pre-prandial glucose levels. RESEARCH DESIGN AND METHODS: In 42 centres, 193 drug-naive (n = 122) or metformin-treated (n = 71) individuals with Type 2 diabetes were randomly allocated to a 40-day period of repaglinide dose-titration (starting at 0.5 mg three times daily) based on either self-measured pre-prandial or post-prandial glucose levels. They were followed for a further 12 weeks and HbA(1c) and hypoglycaemia rates were recorded. RESULTS: Repaglinide reduced HbA(1c) by 1.25 and 1.07% in the post-prandial and pre-prandial groups, respectively (P for difference = 0.6), and achieved target glucose levels in 80.7 and 66.7%, respectively (P = 0.16). The effect of titration strategy differed by baseline drug therapy, and was more effective in the metformin-treated individuals who experienced a HbA(1c) fall of 0.6 vs. 1.10% with pre-prandial vs. post-prandial titration (P for metformin-allocated group interaction = 0.043). The rate of hypoglycaemia did not differ by group. CONCLUSIONS: In drug-naive individuals with Type 2 diabetes, similar HbA(1c) levels are achieved with repaglinide when dosing is adjusted according to either post-prandial or pre-prandial levels. Conversely, in metformin-treated individuals, repaglinide dosing according to post-prandial levels may lead to better glycaemic control than dosing according to pre-prandial levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".