Postpartum Diabetes Screening and Conversion Rates Among Women Diagnosed With Gestational Diabetes Mellitus Using Standard vs Modified Criteria During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Our aim in this work was to examine postpartum diabetes screening and rates in women diagnosed with gestational diabetes mellitus (GDM) using standard vs modified criteria during the COVID-19 pandemic. METHODS: Women with GDM pregnancies between January 1, 2020, and December 31, 2021, in Alberta, Canada, were stratified by the GDM diagnosis criteria and followed for 18 months postpartum diabetes screening. Proportions of prediabetes and diabetes were compared between the standard vs modified GDM criteria groups at 6 and 18 months. Multivariable logistic regression analysis was used to examine differences in prediabetes and diabetes rates between the 2 GDM criteria groups after adjusting for baseline differences. RESULTS: Among 10,238 individuals with GDM, 780 were diagnosed using the modified criteria and 9,458 were diagnosed using the standard criteria. There was no difference in the proportion of individuals who underwent postpartum screening by 6 months (27.1% vs 28.9%, p=0.29) or by 18 months (43.1% vs 45.3%, p=0.24) among the modified and standard groups, respectively. Diabetes proportions were higher in women diagnosed with GDM using the modified criteria compared with those diagnosed using the standard criteria (27.0% vs 4.2%, p<0.0001; adjusted odds ratio 8.18, 95% confidence interval 5.76 to 11.6). Proportions of prediabetes and diabetes at 18 months were 15.2% and 20.8% (p=0.014) and 29.8% and 6.1% (p<0.0001) for modified and standard groups, respectively. CONCLUSIONS: Regardless of the GDM diagnostic method, postpartum diabetes screening among women with GDM was suboptimal during the COVID-19 pandemic. The modified criteria for GDM identified a group of women who were at higher risk for conversion to diabetes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".