Undiagnosed type 2 diabetes during pregnancy is associated with increased perinatal mortality: a large population‐based cohort study in Ontario, Canada
Bibliographic record
Abstract
AIM: To compare perinatal outcomes in women with undiagnosed diabetes with gestational diabetes alone, pre-existing diabetes and women without diabetes, and to identify risk factors which distinguish them from women with gestational diabetes alone. METHODS: This population-based cohort study included administrative data on all women who gave birth in Ontario, Canada, during 2002-2015. Maternal/neonatal outcomes were compared across groups using logistic regression, adjusting for confounders. A nested case control study compared women with undiagnosed type 2 diabetes with women with gestational diabetes alone to determine risk factors that would help identify these women. RESULTS: compared with gestational diabetes alone. Infants had a higher risk of perinatal mortality (OR 2.3 [1.6-3.4]), preterm birth (OR 2.6 [2.3-2.9]), congenital anomalies (OR 2.1 [1.7-2.5]), neonatal intensive care unit admission (OR 3.1 [2.8-3.5]) and neonatal hypoglycaemia (OR 406.0 [357-461]), which were similar to women with pre-existing diabetes. The strongest predictive risk factors included early gestational diabetes diagnosis, previous gestational diabetes and chronic hypertension. CONCLUSIONS: Women diagnosed with gestational diabetes who develop diabetes within 1 year postpartum are at higher risk of adverse pregnancy outcomes, including perinatal mortality. This highlights the need for earlier diagnosis, preferably pre-pregnancy, and more aggressive treatment and surveillance of suspected type 2 diabetes during pregnancy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".