Patterns of glucose‐lowering therapies and neonatal outcomes in the treatment of gestational diabetes in Canada, 2009–2014
Bibliographic record
Abstract
AIM: To examine patterns of use of different glycaemic control agents for treating gestational diabetes mellitus. METHODS: This was a large, retrospective, population-based cohort study of pregnant women with gestational diabetes from Alberta, Canada. We linked data from the Alberta Vital Statistics - Birth database with administrative claims data. Alberta Vital Statistics - Birth data were used to identify births that occurred between 1 January 2009 and 31 December 2014. We used International Classification of Diseases version 9/10 codes to identify women with gestational diabetes, and we excluded women with pre-existing diabetes. RESULTS: Our cohort consisted of 16 857 women with gestational diabetes, with a total of 18 761 birth events between 2009 and 2014. Over the study period, the proportion of women with gestational diabetes who were treated with glycaemic control therapies increased from 25.0% to 31.4% (P<0.0001). The number of pregnancies treated with insulin only increased (from 23.6% to 28.3%; P<0.0001), as did the number treated with metformin, +/- insulin (from 1.4% to 3.2%; P<0.0001). Rates of large-for-gestational-age infants were significantly higher among pregnancies treated with insulin only (17%) or metformin (16.5%) than among pregnancies that did not receive any pharmacological treatment (12.8%). CONCLUSIONS: Our findings show increasing use of insulin and metformin in women with gestational diabetes. Rates of large-for-gestational-age infants were similar among pregnant women receiving either pharmacological treatment, and higher than among pregnant women who did not receive any pharmacological treatment. Future research should explore the long-term outcomes and safety of metformin as an alternative for treating gestational diabetes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".