Rising Prevalence of Gestational Diabetes Mellitus in Ontario: A Population-based Study
Bibliographic record
Abstract
OBJECTIVES: Gestational diabetes mellitus (GDM) is a common pregnancy complication. Studies have shown that the prevalence of GDM is rising worldwide. In this study, we aimed to describe the prevalence of GDM in Ontario, Canada, between 2015 and 2021. METHODS: Population-based linked health-care administrative databases were used to identify women with GDM via a validated algorithm. Age-standardized GDM prevalence was described for each year between 2015 and 2021. Crude GDM prevalence trends were stratified according to age and income, and trend over time was evaluated using negative binomial regression. RESULTS: Crude GDM prevalence was 9.5% within this period, with age-standardized prevalence increasing by 35% over the duration of the study (p<0.0001). Prevalence declined in the first year of the COVID-19 pandemic, but it rose again the next year. Prevalence was directly associated with age (p<0.0001) and inversely associated with income (p=0.04), but these disparities did not change over time. CONCLUSIONS: GDM prevalence is rising, but the transient decline in the first year of the pandemic may reflect forgone GDM screening. Disparities in prevalence by age and income are not worsening. GDM is creating a growing burden for the health-care system, particularly for lower income individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".