Sex and Race/Ethnicity–Related Disparities in Care and Outcomes After Hospitalization for Coronary Artery Disease Among Older Adults
Bibliographic record
Abstract
BACKGROUND: It is unclear to what extent cardiovascular health disparities exist and can be modified among sexes, racial/ethnic groups, and geographic regions in US hospitals. METHODS AND RESULTS: We conducted a cohort study of 49 358 patients aged 65 years and older, admitted to 366 US hospitals from 2003 to 2009 as part of the Get With The Guidelines--Coronary Artery Disease registry linked with Medicare inpatient data. We examined mortality disparities of sex, race/ethnicity, and geographic region with 3-year mortality. The mediator was defined as receiving optimal quality of care. Logistic regression with generalized estimating equations and mediation analysis were used. Compared with men, women were less likely to receive optimal care (odds ratio=0.92; 95% confidence interval: 0.88-0.95; P<0.0001) and more likely to have higher mortality if they received suboptimal care (odds ratio=1.25; 95% confidence interval: 1.00-1.55; P=0.05, P for interaction=0.04). Approximately 69% of the sex disparity may potentially be reduced by providing optimal quality of care to women. Quality of care did not differ across racial/ethnic groups or geographic regions. Blacks were more likely to die than whites (odds ratio=1.33; 95% confidence interval: 1.21-1.46; P<0.0001), and this disparity persisted regardless of the quality of care received. CONCLUSIONS: Women were less likely than men to receive optimal care at discharge. The observed sex disparity in mortality could potentially be reduced by providing equitable and optimal care. In contrast, the higher mortality observed in black patients could not be accounted for by differences in the quality of care measured in this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".