Disparities in Severe Maternal Morbidity and Mortality—A Call for Inclusion of Disability in Obstetric Research and Health Care Professional Education
Bibliographic record
Abstract
Despite advances in medicine, rates of adverse maternal outcomes, such as severe maternal morbidity and maternal mortality, are increasing in high-income countries.1 Of particular concern is the observation that such increases include widening disparities among underserved and minoritized populations. 1 Elsewhere in JAMA Network Open, Gleason et al 2 add to the literature on maternal health disparities by using data from the Consortium on Safe Labor to examine the risks of obstetric interventions and adverse maternal outcomes, including severe maternal morbidity and maternal mortality, among 2142 women with a physical, sensory, or intellectual disability compared with 221 252 women without disabilities.They found women with disabilities were at elevated risk of a range of obstetric interventions, including cesarean delivery (adjusted relative risk [aRR], 1.33; 95% CI, 1.25-1.42),and adverse maternal outcomes, including individual markers of severe maternal +
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.028 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".