Prevalence of gestational diabetes in the United States and Canada: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: An understanding of the prevalence of gestational diabetes mellitus among pregnant women is essential at local, national and international level so that appropriate health care interventions can be planned, financed and delivered. METHODS: A systematic review and meta-analysis of primary research reporting the prevalence of gestational diabetes mellitus in Canada or the United States were carried out according to Meta-analysis of Observational Studies in Epidemiology guidelines. Four electronic databases were systematically searched in June 2023 to identify articles that reported gestational diabetes mellitus prevalence using universal screening in pregnant women from eligible general population samples. Estimates were combined using a random effects model, and the effects of moderator variables analysed. RESULTS: There were 36 separate samples of women or deliveries (total sample size 1,550,917). Overall mean prevalence of gestational diabetes mellitus was 6.9% (95% CI: 5.7-8.3); 13.7% (95% CI: 10.7-17.3) in studies using a one-step screening strategy, and 5.2% (95% CI: 4.4-6.1) in those using a two-step strategy. Heterogeneity in technical methods between studies produced differences in estimates, as did different diagnostic thresholds used. CONCLUSIONS: The meta-analysis suggests a slightly higher prevalence of gestational diabetes mellitus in Canada and the United States, compared to Europe, but highlights the need for standardised protocols for estimating gestational diabetes mellitus prevalence.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".