Impact of a modified screening approach during the <scp>COVID</scp>‐19 pandemic on the diagnosis and outcomes of gestational diabetes mellitus: A population‐level analysis of 90,518 pregnant women
Bibliographic record
Abstract
AIMS: To provide real-world evidence on the uptake of and outcomes associated with the modified gestational diabetes mellitus (GDM) screening approach offered during the COVID-19 pandemic compared with the standard screening approach. METHODS: All pregnancies between 01 January 2020 and 31 December 2021, in Alberta, Canada, were included in the study. We examined GDM screening and diagnosis rates, and large-for-gestational-age (LGA) outcomes. RESULTS: Annual GDM screening rates were > 95% during the study time period. Overall, 84.7%, and 11.6% of the 92,505 pregnancies underwent standard and modified screening for GDM, respectively. The use of modified screening was the highest among deliveries in August 2020 (49.8%) which corresponded to the early first wave of the pandemic. GDM diagnosis rate was lower in the modified screening (7.4%) than in the standard screening (12.3%, p < 0.001) group. The LGA rates in the modified screening with GDM and the standard screening with GDM groups were 24.8% and 12.6%, respectively (p < 0.001). Women in the modified screening with GDM group were at a higher risk of having an LGA infant (adjusted odds ratio: 3.46; 95% confidence interval: 2.93, 4.08) compared to the standard screening with no GDM group. CONCLUSIONS: The COVID-19 epidemic had no impact on screening for GDM. Women who underwent modified screening, based on HbA1c/random plasma glucose, had lower rates of GDM cases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".