The impact on pregnancy outcomes of late‐onset gestational diabetes mellitus diagnosed during the third trimester: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Evidence is inconsistent regarding the impact of late gestational diabetes mellitus (GDM) on perinatal outcomes. OBJECTIVES: To evaluate associations of GDM diagnosed in the third trimester (late GDM) with adverse obstetric and neonatal outcomes. SEARCH STRATEGY: We searched Embase, Medline, and Web of Science from January 1, 1990 to June 16, 2022, for observational studies. SELECTION CRITERIA: Late GDM was defined as a de novo diagnosis, i.e. after a negative screening for diabetes in the second trimester, and at later than 28 weeks of pregnancy. DATA COLLECTION AND ANALYSIS: Each abstract and full-text article was independently reviewed by the same two authors. Quality was assessed with the use of the Newcastle-Ottawa Scale. Summary odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a random effects model. MAIN RESULTS: Twelve studies were identified as meeting the inclusion criteria, including 3103 patients (571 with late GDM and 3103 controls). Incidences of shoulder dystocia (OR 1.57, 95% CI 1.02-2.42, P = 0.040), 5-minute Apgar score <7 (OR 1.80, 95% CI 1.14-2.86, P = 0.024), cesarean delivery (OR 1.98, 95% CI 1.51-2.60, P < 0.001), and emergent cesarean delivery (OR 1.57, 95% CI 1.02-2.40, P = 0.040) were significantly higher among women with late GDM than among the controls. The groups showed similarity in the rates of fetal macrosomia, large-for-gestational-age fetuses, neonatal hypoglycemia, and hypertensive disorders of pregnancy. CONCLUSIONS: This meta-analysis showed associations of late GDM with increased adverse perinatal outcomes. Prospective studies should evaluate the impact on perinatal outcomes of repeated third-trimester screening for late GDM.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| 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".