1415-P: Gestational Diabetes Mellitus after Delivery: The Need for Continued Vigilance
Bibliographic record
Abstract
Despite guideline recommendations, there are currently limited data on the extent and yield of repeat postpartum glucose testing of women with gestational diabetes mellitus (GDM). Accordingly, we examined these issues in a large population-based cohort of women in Alberta, Canada, who gave birth between 10/01/2008-06/30/2013, were diagnosed with GDM, and followed-up until 12/31/2015 (n=10520). The study population included the 4625 (44%) women who underwent glucose testing within 6 months (m) after delivery. Laboratory data were used to identify rates of repeat testing between 6-18 m postpartum. Diabetes Canada thresholds were used to identify women who converted to diabetes mellitus (DM). A total of 211 (4.6%) women had overt DM, i.e., DM within 6 m of delivery. Among 4414 women with GDM who had a normal glucose test within 6 m, 1656 (37.5%) underwent repeat testing between 6-18 m. Of these, 77 (4.6%) were diagnosed with DM. This latter group was older, more likely multiparous, had higher rates of gestational hypertension and C-section, more likely to have been on insulin therapy during pregnancy, and had the highest rates of large for gestational age (LGA) babies (Table). While early postpartum glucose screening is likely to identify women with overt diabetes, continued monitoring, despite initial normal screens, is necessary to ensure early detection of DM and other chronic disease in women with GDM. Disclosure P. Kaul: None. A. Savu: None. L.E. Moore: None. R.O. Yeung: Advisory Panel; Self; Sanofi. Consultant; Self; Novo Nordisk Inc. Research Support; Self; AstraZeneca. Speaker's Bureau; Self; Novo Nordisk Inc., Sanofi. E.A. Ryan: None. Funding Canadian Institutes of Health Research
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".