Treating prediabetes with metformin: systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: To determine if the use of metformin in people with prediabetes (impaired glucose tolerance or impaired fasting glucose) would prevent or delay the onset of frank type 2 diabetes mellitus. DATA SOURCES: MEDLINE was searched from January 1966 to the present, and articles meeting the selection criteria were hand searched. STUDY SELECTION: Randomized controlled trials that involved administration of metformin to delay or prevent type 2 diabetes in individuals with impaired glucose tolerance or impaired fasting glucose were included. Development of diabetes was a required outcome measure; follow-up time of at least 6 months was required. Three studies met these criteria. SYNTHESIS: The 3 studies varied in ethnicity of the population studied, in the rates of conversion to diabetes from prediabetes, and in the dose of metformin used. In general the studies were well done, although 2 of the 3 did not do true intention-to-treat analyses. A sensitivity analysis was completed by converting all data to intention-to-treat data and assuming a worst-case scenario for the people who were lost to follow-up. CONCLUSION: Metformin decreases the rate of conversion from prediabetes to diabetes. This was true at higher dosage (850 mg twice daily) and lower dosage (250 mg twice or 3 times daily); in people of varied ethnicity; and even when a sensitivity analysis was applied to the data. The number needed to treat was between 7 and 14 for treatment over a 3-year period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".