The relationship between timing of screening for gestational diabetes mellitus and maternal and fetal outcomes: A retrospective cohort study linking primary care electronic and hospital administrative data
Bibliographic record
Abstract
Background: Gestational diabetes (GDM) is associated with adverse outcomes including a large-for-gestational age (LGA) baby, which in turn is associated with downstream childhood obesity. Appropriate timing of GDM screening is important for prompt initiation and optimization of medical management, potentially mitigating the risk of those outcomes. The present study explored the association between the timing of GDM screening and macrosomia, LGA, shoulder dystocia and caesarean section. Methods: This retrospective cohort study linked primary care prenatal data and intrapartum data from a provincial hospital administrative database. Women with singleton pregnancies who received prenatal care between July 1, 2019 and December 31, 2022 and who also delivered within that timeframe were included in the study. Results: 198 participants were linked between the databases. Among participants for whom GDM risk could be calculated (n = 180), 30.6 % had late GDM screening. Unadjusted logistic regression models showed that late screening for GDM was associated with higher likelihood of LGA (OR = 2.89; 95 % CI = 1.19-7.04; p = 00.019). Adjusted models showed that the best predictor of macrosomia, LGA, and shoulder dystocia was excess gestational weight gain (GWG) (OR = 3.26, CI = 1.17-9.10, p = 0.024; OR 3.00, 95 % CI 0.91-9.93, p = 00.072; and OR = 3.52, CI = 0.83-14.84, p = 00.087 respectively); the best predictor of caesarean section was pre-pregnancy BMI (OR = 2.86; CI = 1.12 = 7.27; p = 0.028). Conclusions: Almost one-third of participants had screening later than recommended, and late screening for GDM was associated with a higher likelihood of LGA. Linking longitudinal prenatal primary care data to hospital administrative data creates opportunities for future studies pertaining to prenatal care, potentially resulting in improvements in the care provided to vulnerable populations experiencing disproportionate rates of pre-pregnancy obesity and excess GWG.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".