Diabetes mellitus in pregnancy across Canada
Bibliographic record
Abstract
BACKGROUND: Contemporary estimates of diabetes mellitus (DM) rates in pregnancy are lacking in Canada. Accordingly, this study examined trends in the rates of type 1 (T1DM), type 2 (T2DM) and gestational (GDM) DM in Canada over a 15-year period, and selected adverse pregnancy outcomes. METHODS: This study used repeated cross-sectional data from the Canadian Institute of Health Information (CIHI) hospitalization discharge abstract database (DAD). Maternal delivery records were linked to their respective birth records from 2006 to 2019. The prevalence of T1DM, T2DM and GDM were calculated, including relative changes over time, assessed by a Cochrane-Armitage test. Also assessed were differences between provinces and territories in the prevalence of DM. RESULTS: Over the 15-year study period, comprising 4,320,778 hospital deliveries in Canada, there was a statistically significant increase in the prevalence of GDM and T1DM and T2DM. Compared to pregnancies without DM, all pregnancies with any form of DM had higher rates of hypertension and Caesarian delivery, and also adverse infant outcomes, including major congenital anomalies, preterm birth and large-for-gestational age birthweight. CONCLUSION: Among 4.3 million pregnancies in Canada, there has been a rise in the prevalence of DM. T2DM and GDM are expected to increase further as more overweight women conceive in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".