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Record W4414168932 · doi:10.1016/j.diabres.2025.112462

Gestational diabetes mellitus and its impact on maternal and neonatal outcomes in Indigenous populations: a systematic review and meta-analysis

2025· article· en· W4414168932 on OpenAlexaboutno aff
Thuy Linh Duong, K. M. Shahunja, Minh Tâm Lê, David McIntyre, James Ward, Abdullah Al Mamun

Bibliographic record

VenueDiabetes Research and Clinical Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersUniversity of Queensland
KeywordsGestational diabetesShoulder dystociaCaesarean sectionPregnancyDiabetes mellitusConfidence intervalBirth weightGestational age

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.135
GPT teacher head0.520
Teacher spread0.385 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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