Cardiovascular and Metabolic Risk in Women in the First Year Postpartum: Allostatic Load as a Function of Race, Ethnicity, and Poverty Status
Bibliographic record
Abstract
OBJECTIVE: Allostatic load (AL) represents multisystem physiological "wear-and-tear" reflecting emerging chronic disease risk. We assessed AL during the first year postpartum in a diverse community sample with known health disparities. STUDY DESIGN: Shriver National Institute for Child Health and Human Development Community Child Health Network enrolled 2,448 predominantly low-income African-American, Latina, and White women immediately after delivery of liveborn infants at ≥20 weeks' gestation, following them over time with interviews, clinical measures, and biomarkers. AL at 6 and 12 months postpartum was measured by body mass index, waist:hip ratio, blood pressure, pulse, hemoglobin A1c, high-sensitive C-reactive protein, total cholesterol and high-density lipoprotein, and diurnal cortisol slope. RESULTS: Adverse AL health-risk profiles were significantly more prevalent among African-American women compared with non-Hispanic Whites, with Latinas intermediate. Breastfeeding was protective, particularly for White women. Complications of pregnancy were associated with higher AL, and disparities persisted or worsened through the first year postpartum. CONCLUSION: Adverse AL profiles occurred in a substantial proportion of postpartum women, and disparities did not improve from birth to 1 year. Breastfeeding was protective for the mother.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".