Racial disparities in gestational weight gain and adverse pregnancy outcomes among Black and White pregnant people with obesity
Bibliographic record
Abstract
OBJECTIVE: This study of pregnant people with obesity examined two aims in testing the hypothesis that the COVID-19 pandemic widened racial disparity in maternal health in high-risk pregnancies; it compared by race both (1) gestational weight gain (GWG) patterns and (2) patterns of preexisting conditions and adverse pregnancy outcomes. METHODS: This retrospective chart review included birth certificate and delivery records from a large women's specialty hospital in Louisiana between 2018 and 2022. Differences in preexisting conditions, GWG, and adverse pregnancy outcomes were explored across early-, peak-, and late-pandemic periods using log-linear regression and robust Poisson models. RESULTS: Among 7431 deliveries (54% Black), Black pregnant people had higher rates of preexisting type 2 diabetes and chronic hypertension but lower rates of gestational diabetes and preeclampsia compared to White pregnant people across all periods. Black individuals had higher prepregnancy weight and lower GWG compared to White individuals across all periods. GWG differences were not significant in peak- and late-pandemic periods. CONCLUSIONS: Black individuals with obesity started pregnancy with higher weight and more preexisting conditions but had lower GWG compared to White individuals. Exacerbated disparities in preexisting conditions demonstrate higher health risks for Black individuals during pregnancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".