Periconceptional folate and gestational diabetes mellitus: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
To examine the relationship between periconceptional folate exposure and risk of gestational diabetes mellitus (GDM). Several electronic databases, including PubMed, Embase, China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), and Cochrane Library, were searched for all relevant cohort studies by January 2021. Studies on relationship between folate exposure (intake or status) and GDM risk were included. Quality of included studies was assessed using Newcastle-Ottawa Scale. Random effects meta-analysis was performed to estimate overall odds ratio (OR) and 95% confidence intervals (CIs) by Stata software (Stata Corp., College Station, TX). Ten cohort studies with 40,244 pregnancies were eligible for quantitative meta-analysis. Significant association was observed between folate exposure and risk of GDM (OR = 1.24, p=.036, 95% CI: 1.01–1.52). Subgroup analysis revealed that periconceptional folate exposure of population in China (OR = 1.35, 95% CI: 1.09–1.67) but not in western countries, folate exposure during pregnancy (OR = 1.49, 95% CI: 1.22–1.81) but not before pregnancy, and internal folate exposure (OR = 1.36, 95% CI: 1.10–1.67), were significantly associated with increased GDM risk. Overall, periconceptional folate exposure is positively associated with GDM risk, especially the exposure during pregnancy and exposure in Chinese populations.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| 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".