Maternal gestational diabetes mellitus and the childhood asthma in offspring: a meta-analysis
Bibliographic record
Abstract
Abstract Background Maternal diabetes might be related to a high risk of allergic disease in offspring. However, it remains unknown if maternal gestational diabetes mellitus (GDM) is also associated with a high incidence of childhood asthma in offspring. A systematic review and meta-analysis was performed to investigate the above association. Methods Relevant observational studies were obtained by search of electronic databases including Medline, Embase, Cochrane Library, and Web of Science. A randomized-effects model was selected to pool the data by incorporating the influence of potential heterogeneity. The Newcastle-Ottawa Scale was used for study quality evaluation. Subgroup analyses were performed to evaluate the potential influences of study characteristics on the outcome. Results Ten datasets from seven moderate to high quality cohort studies, involving 523,047 mother-child pairs were included in the meta-analysis. Overall, maternal GDM was associated with a higher risk of childhood asthma in offspring (risk ratio [RR]: 1.22, 95% confidence interval [CI]: 1.07 to 1.39, p = 0.003; I 2 = 30%). Subgroup analyses showed that the association was not significantly affected by study design, validation methods for GDM, or diagnostic strategy for asthma (p for subgroup analyses all > 0.05). The association between maternal GDM and asthma in offspring was more remarkable after adjusting maternal body mass index in early pregnancy (RR: 1.50 versus 1.06, p < 0.001), but significantly weakened after adjusting hypertensive disorders during pregnancy (RR: 1.08 versus 1.50, p = 0.001). Conclusions Maternal GDM may be associated with an increased incidence of childhood asthma in offspring.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.046 |
| Bibliometrics | 0.005 | 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) | 0.003 | 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".