Race/ethnic disparities in risk factor control and survival in the bypass angioplasty revascularization investigation 2 diabetes (BARI 2D) trial.
Bibliographic record
Abstract
This study sought to evaluate the impact of race/ethnicity on cardiovascular risk factor control and on clinical outcomes in a setting of comparable access to medical care. The BARI 2D trial enrolled 1,750 participants from the United States and Canada that self-reported either White non-Hispanic (n [ 1,189), Black non-Hispanic (n =349), or Hispanic (n =212) race/ethnicity. Participants had type 2 diabetes and coronary artery disease and were randomized to cardiac and glycemic treatment strategies. All patients received intensive target-based medical treatment for cardiac risk factors. Average follow-up was 5.3 years. Kaplan-Meier survival curves and Cox proportional hazards regression models were constructed to assess potential differences in mortality and cardiovascular outcomes across racial/ethnic groups. Long-term risk of death and death/myocardial infarction/stroke did not vary significantly by race/ethnicity (5-year death: 11.0% Whites, 13.7% Blacks, 8.7% Hispanics, p =0.19; adjusted hazard ratio 1.18 Black versus White, 95% confidence interval 0.84 to 1.67, p = 0.33 and 0.82 Hispanic versus White, 95% confidence interval 0.51 to 1.34, p =0.43). Among the 1,168 patients with suboptimal risk factor control at baseline, the ability to attain better risk factor control during the trial was associated with higher 5-year survival (71%, 86% and 95% for patients with 0 or 1, 2, and 3 factors in control, respectively, p <0.001); this pattern was observed within each race/ethnic group. In conclusion, significant race/ethnic differences in cardiac risk profiles that persisted during follow-up did not translate into significant differences in 5-year death or death/MI/stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".