Maternal and Neonatal Morbidity for Women Who Would Be Added to the Diagnosis of GDM Using IADPSG Criteria: A Secondary Analysis of the Hyperglycemia and Adverse Pregnancy Outcome Study
Bibliographic record
Abstract
OBJECTIVE: To assess the frequency of adverse outcomes for women who are diagnosed with gestational diabetes mellitus (GDM) by the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria using data from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. RESEARCH DESIGN AND METHODS: This is a secondary analysis from the North American HAPO study centers. Glucose measurements from a 75-g oral glucose tolerance test were used to group participants into three nonoverlapping categories: GDM based on Carpenter-Coustan (CC) criteria (also GDM based on IADPSG criteria), GDM diagnosed based on IADPSG criteria but not CC criteria, and no GDM. Newborn outcomes included birth weight, cord C-peptide, and newborn percentage fat above the 90th percentile; maternal outcomes included primary cesarean delivery and preeclampsia. Outcome frequencies were compared using multiple logistic regression, adjusting for predefined covariates. RESULTS: Among 25,505 HAPO study participants, 6,159 blinded participants from North American centers were included. Of these, 81% had normal glucose testing, 4.2% had GDM based on CC criteria, and 14.3% had GDM based on IADPSG criteria but not CC criteria. Compared with women with no GDM, those diagnosed with GDM based on IADPSG criteria had adjusted odds ratios (95% CIs) for birth weight, cord C-peptide, and newborn percentage fat above the 90th percentile, as well as primary cesarean delivery and preeclampsia, of 1.87 (1.50-2.34), 2.00 (1.54-2.58), 1.73 (1.35-2.23), 1.31 (1.07-1.60), and 1.73 (1.32-2.27), respectively. CONCLUSIONS: Women diagnosed with GDM based on IADPSG criteria had higher adverse outcome frequencies compared with women with no GDM. These data underscore the need for research to assess the effect of treatment to improve outcomes in such women.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".