Impact of Race on the In‐Hospital Quality of Care Among Young Adults With Acute Myocardial Infarction
Bibliographic record
Abstract
Background The extent to which race influences in‐hospital quality of care for young adults (≤55 years) with acute myocardial infarction (AMI) is largely unknown. We examined racial disparities in in‐hospital quality of AMI care and their impact on 1‐year cardiac readmission. Methods and Results We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study enrolling young Black and White US adults with AMI (2008–2012). An in‐hospital quality of care score (QCS) was computed (standard AMI quality indicators divided by the total a patient is eligible for). Multivariable logistic regression was performed to identify factors associated with the lowest QCS tertile, including interactions between race and social determinants of health. Among 2846 young adults with AMI (median 48 years [interquartile range 44–52], 67.4% women, 18.8% Black race), Black individuals, especially women, exhibited a higher prevalence of cardiac risk factors and social determinants of health and were more likely to experience a non–ST‐segment–elevation myocardial infarction than White individuals. Black individuals were more likely in the lowest QCS tertile than White individuals (40.8% versus 34.7%; P =0.003). The association between Black race and low QCS (odds ratio [OR], 1.25; 95% CI, 1.02–1.54) was attenuated by adjustment for confounders. Employment was independently associated with better QCS, especially among Black participants (OR, 0.76; 95% CI, 0.62–0.92; P‐ interaction =0.02). Black individuals experienced a higher rate of 1‐year cardiac readmission (29.9% versus 20.0%; P <0.0001). Conclusions Black individuals with AMI received lower in‐hospital quality of care and exhibited a higher rate of cardiac readmissions than White individuals. Black individuals had a lower quality of care if unemployed, highlighting the intersection of race and social determinants of health.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".