1362-P: Influence of Ethnicity on the Association between Body Mass Index and Prevalence of Gestational Diabetes
Bibliographic record
Abstract
Gestational diabetes (GDM) prevalence increases with increasing body mass index (BMI), and evidence suggests that the association may vary by ethnicity. Using a cross-sectional study, we examined the association between BMI and GDM by ethnicity. Data were obtained from administrative health datasets for all of Ontario, Canada. Using a validated algorithm, we identified Chinese and South Asian maternal ethnicity; all others were categorized as the general population. GDM was ascertained through hospital and claims records. The study population consisted of women without pre-existing diabetes who had a livebirth between April 2012 and March 2014. The relation between pre-pregnancy BMI and GDM was modeled using adjusted logistic regression, stratified by ethnicity. The study population consisted of 231,618 women, of whom 10,895 (4.7%) developed GDM. The prevalence of GDM was 9.9% among South Asians, 8.2% among Chinese and 4.3% of the general population of women. Compared to a normal BMI, having an overweight BMI (25-30 kg/m2) was associated with a higher aOR of GDM among Chinese (1.96, 95% CI: 1.50-2.56), South Asian (1.87, 1.47-2.36) and the general population of women (1.84, 1.69-2.01) (Figure). GDM prevalence is considerably higher across all levels of BMI in South Asian and Chinese women compared with the general population, suggesting the need for an ethnicity-specific screening approach for GDM. Disclosure S. Read: None. H. Berger: None. D. Feig: Advisory Panel; Self; Novo Nordisk A/S. Speaker’s Bureau; Self; Medtronic. K. Fleming: None. J.G. Ray: None. B.R. Shah: None. L. Lipscombe: None. Funding Diabetes Action Canada (PSI19-23)
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".