Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study
Bibliographic record
Abstract
OBJECTIVE: To compare associations of maternal glucose and A1C with adverse outcomes in the multinational Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and determine, based on those comparisons, if A1C measurement can provide an alternative to an oral glucose tolerance test (OGTT) in pregnant women. RESEARCH DESIGN AND METHODS: Eligible pregnant women underwent a 75-g OGTT at 24-32 weeks' gestation. A sample for A1C was also collected. Neonatal anthropometrics and cord serum C-peptide were measured. Associations with outcomes were assessed using multiple logistic regression with adjustment for potential confounders. RESULTS: Among 23,316 HAPO Study participants with glucose levels blinded to caregivers, 21,064 had a nonvariant A1C result. The mean ± SD A1C was 4.79 ± 0.40%. Associations were significantly stronger with glucose measures than with A1C for birth weight, sum of skinfolds, and percent body fat >90th percentile and for fasting and 1-h glucose for cord C-peptide (all P < 0.01). For example, in fully adjusted models, odds ratios (ORs) for birth weight >90th percentile for each measure higher by 1 SD were 1.39, 1.45, and 1.38, respectively, for fasting, 1-, and 2-h plasma glucose and 1.15 for A1C. ORs for cord C-peptide >90th percentile were 1.56, 1.45, and 1.35 for glucose, respectively, and 1.32 for A1C. ORs were similar for glucose and A1C for primary cesarean section, preeclampsia, and preterm delivery. CONCLUSIONS: On the basis of associations with adverse outcomes, these findings suggest that A1C measurement is not a useful alternative to an OGTT in pregnant 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".