Maternal Race, Procedures, and Infant Birth Weight in Type 2 and Gestational Diabetes
Bibliographic record
Abstract
OBJECTIVE: To examine the relation between race and cesarean delivery, episiotomy, and low birth weight infants in pregnancies with type 2 and gestational diabetes mellitus and to identify factors that might explain racial differences. METHODS: Population-based, cross-sectional study of 1999-2004 Maryland hospital discharge data. Hospitalizations for delivery of pregnancies with type 2 and gestational diabetes mellitus were identified and matched to infants. The independent variable was maternal race. Dependent variables were cesarean delivery, episiotomy, and low infant birth weight. Stepwise logistic regression models were developed to estimate the independent effect of race on use of each procedure and infant birth weight, after adjusting for sociodemographic, hospital, and clinical factors. RESULTS: We examined 6,310 deliveries for pregnancies with type 2 (15%) and gestational (85%) diabetes. Before adjustment, black race was associated with a higher odds of cesarean delivery (odds ratio [OR] 1.40, 95% confidence interval [CI] 1.24-1.58) and low birth weight infants (OR 1.94, 95% CI 1.57-2.40) compared with white race. Adjustment for racial differences in preeclampsia and fetal heart rate abnormalities accounted for a modest degree of the racial variation in outcomes. With full adjustment, black race was still associated with a higher odds of cesarean delivery (OR 1.38, 95% CI 1.20-1.60) and low birth weight (OR 1.81, 95% CI 1.41-2.34) and a lower odds of episiotomy (OR 0.45, 95% CI 0.36-0.57). CONCLUSION: In pregnancies with diabetes, adjustment for sociodemographic, hospital, and clinical factors only partially explains racial differences in procedure use and infant low birth weight.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".