Gestational diabetes: One size does not fit all—an observational study of maternal and neonatal outcomes by maternal glucose profile
Bibliographic record
Abstract
OBJECTIVES: To examine obstetrical and neonatal outcomes across maternal glucose profiles at the population level and to explore insulin sensitivity and beta-cell function across profiles in an independent, well-phenotyped cohort for potential pathophysiologic explanation. RESEARCH DESIGN AND METHODS: Observational cohort study of all pregnancies with gestational diabetes screening between October 2008 and December 2018 resulting in live singleton birth in Alberta, Canada (n = 436,773) were categorized into seven maternal glucose profiles: (1) normal 50 g-glucose challenge test (nGCT), (2) normal 75-g OGTT (nOGTT), (3) isolated elevated 1 h post-load glucose (ePLPG1), (4) isolated elevated 2 h post-load glucose (ePLPG2), (5) elevated 1 and 2 h post-load glucose (ePLPG12), (6) isolated elevated FPG (eFPG), and (7) elevated FPG + elevated 1-h and/or 2-h PLG (Combined). Primary outcomes were large for gestational age (LGA) and neonatal intensive care unit (NICU) admission rates. An independent observational cohort of 1451 women was examined for measures of beta-cell function (ISSI-2, insulinogenic index/HOMA-IR) and insulin sensitivity/resistance (Matsuda index, HOMA-IR) by similar maternal glucose profiles. RESULTS: Pregnancies with elevated FPG, either isolated or combined, had higher adverse events and lower insulin sensitivity. The combination of elevated FPG + elevated 1-h and/or 2-h PLG had the highest rates of LGA(20.9%), NICU admissions (14.7%), and lowest insulin sensitivity as measured by Matsuda index and HOMA-IR, and beta-cell function as measured by ISSI-2 and Insulinogenic index/HOMA-IR. CONCLUSIONS: Elevated fasting plasma glucose, either alone or combined with post-load glucose elevation is associated with worse outcomes than isolated post-load glucose elevation, possibly due to higher degrees of insulin resistance. Future work is needed to better understand these differences, and explore whether tailored treatment of GDM can improve neonatal 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".