Prevalence and Timing of Screening and Diagnostic Testing for Gestational Diabetes Mellitus: A Population-Based Study in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: The extent to which pregnant women are screened for gestational diabetes mellitus (GDM) at the population level is not known. We examined the rate, type, and timing of GDM screening and diagnostic testing in the province of Alberta, Canada. Geographic and temporal differences in screening rates, and maternal risk factors associated with lower likelihood of screening, were also determined. RESEARCH DESIGN AND METHODS: Our retrospective linked-database cohort study included 86,842 primiparous women with deliveries between 1 October 2008 and 31 December 2012. Multivariable logistic regression analysis was used to examine maternal factors associated with lower likelihood of GDM screening. RESULTS: Overall, 94% (n = 81,304) of women underwent some form of glycemic assessment in the 270 days prior to delivery. The majority (91%) received a 50-g glucose screen (GDS). Women not screened were younger and more likely to smoke and had lower maternal weight and median household income. When a diagnostic 75-g oral glucose tolerance test (OGTT) was indicated, it occurred a median of 10 (interquartile range 7, 15) days after the screen. CONCLUSIONS: GDS occurred widely in a system where it was universally recommended and paid for publicly. When indicated, a 75-g OGTT was completed within 15 days in 75% of cases. Our finding that this two-step approach was widely implemented in a timely fashion supports continued endorsement of a two-step approach to screening and diagnosis of GDM. Further research is merited to assess whether the one-step GDM diagnostic approach results in different rates and timing of the 75-g OGTT and affects pregnancy outcomes.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".