Abstract 13253: Race, Exercise Training and Outcomes in Chronic Heart Failure
Bibliographic record
Abstract
Background: The strength of race as a predictor of outcomes in a contemporary chronic heart failure (HF) population and its interaction with exercise training response remains unexplored. Methods: We performed an analysis of HF-ACTION, which randomized 2,331 HF patients with EF Results: Of patients with race documented, 62% (N=1426) were white, 33% (N=749) were AA and 5% (N=121) were other. Compared with whites, AAs were younger (median 55 vs. 62 yr), more often female (40% vs. 22%), and less often had ischemic HF (32% vs. 61%), but had a similar EF and creatinine. At baseline, AAs had a lower peak VO2 (raw median 13.2 vs. 15.0 mL/kg/min) and shorter 6-minute walk (raw median 348 vs. 383 meters) despite gender adjustment (both P P =0.032) and CV mortality/HF hosp ( P =0.0002). After multivariable adjustment including education and income level, AA race was associated with increased CV mortality/CV hosp and CV mortality/HF hosp, but not increased mortality ( Table ). There was no significant interaction between race and exercise training on outcomes (all P >0.5). Conclusion: AA race in chronic HF patients with reduced EF was associated with younger age, higher proportion of women and non-ischemic etiology, reduced baseline exercise capacity, and increased CV mortality/CV hosp and CV mortality/HF hosp, but not increased all-cause mortality or a differential response to exercise training on clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".