Endometriosis and gestational diabetes mellitus risk: a systematic review and meta-analysis
Bibliographic record
Abstract
To perform a systematic review and meta-analysis regarding endometriosis and the risk of gestational diabetes mellitus (GDM).We carried out a search of the following databases: Medline, Embase, Web of Science, Cochrane Library, Scopus, Scielo, Clinicaltrials.gov, the UK Clinical Trials Gateway, and the Australian New Zealand Clinical Trials Registry, from inception through April 28 2017, without language restrictions, in order to evaluate the effect of endometriosis over GDM risk, in women with and without endometriosis. Odds ratios (ORs) and their 95% confidence intervals (CIs) or mean differences (MDs) were calculated as effects. Methodological quality of evidence was assessed with the Newcastle-Ottawa Scale, and heterogeneity among studies with the I2 statistic. Random-effects models were used for meta-analyses, and publication bias was assessed with Egger's test.We identified 12 studies (10 cohort and two case control studies) with a total of 48,762 pregnancies, including 3,461 with endometriosis. Endometriosis had no significant effect on GDM risk (OR =1.14; 95% CI: 0.86, 1.51; p = .35, I2 = 56%, Egger's test p = .45). Secondary outcomes (gestational age at delivery, birthweight, and Neonatal Intensive Care Unit admission) were statistically similar in women with and without endometriosis.Better-designed studies are needed to confirm our results.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".