Gestational diabetes mellitus and its impact on maternal and neonatal outcomes in Indigenous populations: a systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis examined the association between gestational diabetes mellitus (GDM) and adverse pregnancy outcomes among Indigenous populations globally. Pooled risk ratios were calculated using a random-effects model, and study quality was assessed using the Newcastle-Ottawa Scale and the CONSIDER Statement. Twenty studies from Canada, the United States, and Australia were included. Results showed that GDM was associated with increased caesarean section (risk ratio 1.83, 95% confidence interval 1.63 to 2.06), shoulder dystocia (3.21, 2.94 to 3.50), large for gestational age (2.35, 1.46 to 3.77), macrosomia (1.75, 1.48 to 2.07), preterm birth (1.36, 1.09 to 1.69), and hypoglycaemia (8.17, 4.39 to 15.22), but decreased risk of low birth weight (0.80, 0.69 to 0.91) and small for gestational age (0.44, 0.39 to 0.50). Four studies had low or medium risk of bias, only 25% of the studies reported Indigenous involvement in the research process. These findings show that Indigenous women with GDM are at greater risk of perinatal complications than those without GDM. This underscores the need for timely, intensive clinical management of GDM, delivered within culturally safe models of care, to reduce these inequities. In line with calls for action, prioritizing the early prevention of GDM is essential.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".