A Retrospective Analysis of Postpartum Glucose Testing Incidence by Prenatal Care Provider Specialty in a Canadian Gestational Diabetes Cohort
Bibliographic record
Abstract
Objectives Gestational diabetes mellitus (GDM) increases future risks of type 2 diabetes and cardiovascular disease. Despite Diabetes Canada guidelines recommending postpartum glucose testing after GDM, uptake remains low. Canadian population-based studies are needed to examine system-level factors affecting uptake. Methods We used linked Alberta Health data sets, including births from 2017 to 2018, to identify prenatal care provider specialty (general practitioner [GP], obstetrician [OB], or midwife [RM]), postpartum glucose tests, and cohort demographics. Outcomes were 1) gold standard testing, which is oral glucose tolerance testing (OGTT) within 6 weeks to 6 months postpartum; and 2) any glucose test within 6 weeks to 1 year. We used adjusted logistic regression modelling to estimate the association between test incidence and provider specialty. Results From 105,691 births, we identified a cohort of 9,884 with GDM. Uptake of postpartum glucose testing was low: 22.2% (95% confidence interval [CI] 21.4% to 23.1%) received gold standard testing and 53.9% (95% CI 52.9% to 54.9%) received any glucose test. When compared with the OB group, GP patients were less likely to receive both glucose test outcomes (odds ratio [OR] GS =0.86, 95% CI 0.77 to 0.95; OR Any =0.88, 95% CI 0.81 to 0.96). Patients of RMs were also less likely to receive both glucose test outcomes (OR GS =0.67, 95% CI 0.46 to 0.96; OR Any =0.88, 95% CI 0.67 to 1.15). Conclusions We report low postpartum glucose testing overall and found no clinically meaningful differences across provider specialties. The higher incidence of any glucose testing suggests that providers may be prioritizing alternative tests over the OGTT protocol for GDM patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".