Effects of a province-wide change in gestational diabetes screening policy on treatment and newborn birth weight
Bibliographic record
Abstract
OBJECTIVE: To evaluate changes in GDM treatment and newborn birthweight after a 2010 change in GDM screening recommendations from a 2-step (50-g glucose challenge + 3-h 100-g oral glucose tolerance test (OGTT) with Carpenter-Coustan criteria) to a mix of 1-step and 2-step (2-h 75-g OGTT with IADPSG criteria). RESEARCH DESIGN AND METHODS: We estimated effects of the screening change on the incidence of lifestyle or medication treatment, infant birth weight greater than the 90th percentile or less than the 10th percentile for gestational age (large- and small-for-gestational age) and endocrinologist visits, using interrupted time series analysis in all 463,881 individuals with singleton pregnancies (>28 gestational weeks) from British Columbia, Canada between 2004 and 2019. RESULTS: After the screening change, lifestyle-treated GDM increased immediately (level change 1.85 95% CI 1.19 to 2.51) corresponding to an immediate 1.85% increase in incidence. Medication-treated GDM increased gradually (trend change 0.23 95% 0.09 to 0.37 per year), but there was no change in medication-treated GDM using a shorter (3 year) post-policy (level change: -0.31 (-0.9, 0.29); trend change: 0.03 (-0.36, 0.43)). We detected no change in infant birth weight outcomes and endocrinology visits. CONCLUSIONS: Changing screening approach substantially increased diagnoses of lifestyle-treated GDM, but did not impact medication-treated GDM or infant birth weight.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".