Abstract 13776: The Impact of Race on In-hospital Quality of Care Among Young Adults With Acute Myocardial Infarction
Bibliographic record
Abstract
Introduction: The extent to which race influences in-hospital quality of care among young adults with acute myocardial infarction (AMI) is unknown. We examined racial differences in in-hospital quality of AMI care in young adults and described the patient and/or clinical characteristics associated with potential disparities in care. Methods: Data from the GENESIS-PRAXY (Canada) and the VIRGO (U.S.) prospective cohorts of young adults with AMI were analyzed. Among a total of 4,048 adults with AMI (≤55 years) (median=49 years [IQR 44-52], 22% non-white, 58% women), we calculated an in-hospital quality of care score (QCS) for AMI (quality indicators divided by total, with higher scores indicating better care) based on AHA quality of care standards, reporting data disaggregated by race. We categorized race as white versus non-white, which included Black, Asian and North American Indigenous populations. Results: This cohort was comprised of 906 non-white individuals and 3142 white individuals. Non-white adults exhibited a clustering of adverse cardiac risk factors, psychosocial risk factors and comorbidities versus whites; they had higher rates of hypertension, diabetes, alcohol abuse and prior AMI and lower rates of physical activity. They were more likely to have a low SES and receive low social support, and were less likely to be employed, a primary earner, or married/living with a partner. Non-white individuals were also more likely to experience a NSTEMI and less likely to receive cardiac rehabilitation, smoking cessation counseling as well as dual antiplatelet therapy at discharge. Furthermore, non-white individuals had a lower crude QCS than whites (QCS=69.99 vs 73.29, P-value<0.0001). In the multivariable model adjusted for clinical and psychosocial factors, non-white race (LS Mean Difference=-1.49 95%CI -2.87, -0.11, P-value=0.0344) was independently associated with a lower in-hospital QCS. Conclusion: Non-white individuals with AMI exhibited higher rates of adverse psychosocial and clinical characteristics than white individuals yet non-white race was independently associated with lower in-hospital quality of care. Interventions are needed to improve quality of AMI care in non-white young adults.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".