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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".