Lifestyle intervention for the prevention of type 2 diabetes in women with prior gestational diabetes: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To determine whether current evidence supports lifestyle intervention for type 2 diabetes (T2D) prevention in women with previous gestational diabetes (GD). METHODS: We systematically searched MEDLINE/PubMed, Web of Science, EMBASE, The Cochrane Library, International Pharmaceutical Abstracts, Global Health, Sinomed and Clinicaltrials.gov for randomized controlled trials (published from 1 January 1950 to 14 December 2022) comparing lifestyle intervention with standard care in women with previous GD. Our primary outcome was incident T2D, with pooled estimates calculated by a fixed-effects model. RESULTS: Of 1652 studies identified, 13 were eligible and were included in our analysis (N = 3745 women). Compared with standard care, lifestyle intervention yielded a reduction of 24% in the incidence of T2D (relative risk 0.76 [95% CI 0.63-0.93]). Meta-regression analyses revealed no impact of the duration of lifestyle intervention (P = .81) or baseline body mass index (P = .90) on the observed reduction in incident T2D. Importantly, this published literature shows evidence of publication bias on funnel plot and Egger test (P = .048). CONCLUSIONS: Current published evidence suggests that lifestyle intervention can reduce the risk of T2D in women with prior GD. However, this finding should be interpreted with caution in the presence of documented publication bias.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".