Risk of Recurrent Adverse Outcomes in Gestational Diabetes: a Retrospective Cohort Study
Bibliographic record
Abstract
Objective: Compare the risk of recurrent adverse delivery outcome (ADO) or adverse neonatal outcome (ANO) between consecutive gestational diabetes (GDM) pregnancies. Design: Retrospective cohort Setting: Sydney, Australia Population or Sample: 424 pairs of consecutive singleton GDM pregnancies, 2003-2015 Main Outcome Measures:. ADO: instrumental delivery and emergency Caesarean. ANO: large for gestational age (LGA), small for gestational age (SGA), and composite ANO (LGA/SGA/stillbirth/neonatal death/shoulder dystocia). Methods: Using each pregnancy pair (“index” and “subsequent” pregnancy), we calculated ADO and ANO rates and determined risk factors for subsequent pregnancy outcomes (multivariate regression). Results: Subsequent pregnancies had higher rates of elective Caesarean (30.4% vs 17.0%, p<0.001) and lower rates of instrumental delivery (5% vs 13.9%, p<0.001), emergency Caesarean (7.1% vs 16.3%, p<0.001) and vaginal delivery (62.3% vs 66.3%, p=0.01). ANO rates in index and subsequent pregnancies did not differ. Index pregnancy adverse outcome was associated with a higher risk of repeat outcome: RR 3.09 (95%CI:1.30, 7.34) for instrumental delivery, RR 2.20 (95%CI:1.06, 4.61) for emergency Caesarean, RR 4.55 (95%CI:3.03, 6.82) for LGA, RR 5.01 (95%CI:2.73, 9.22) for SGA and RR 2.10 (95%CI:1.53, 2.87) for composite ANO). The greatest risk factor for subsequent LGA (RR 3.13 (95%CI:2.20, 4.47)), SGA (RR 4.71 (95%CI:2.66, 8.36)) or composite ANO (RR 2.01 (95%CI:1.46, 2.78)) was having the same outcome in the index pregnancy. Conclusions: Women with GDM and an adverse outcome are at very high risk of the same complication in their subsequent GDM pregnancy, representing a high-risk group that should be targeted for directed management over routine care.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".