Effects of Right Ventricular Ejection Fraction on Outcomes in Chronic Systolic Heart Failure
Bibliographic record
Abstract
BACKGROUND: Studies of the effect of right ventricular ejection fraction (RVEF) on outcomes in heart failure (HF) are limited by small sample size and short follow-up. METHODS AND RESULTS: We examined the effect of baseline RVEF on outcomes in 2008 Beta-Blocker Evaluation of Survival Trial (BEST) participants with HF and left ventricular ejection fraction or=40% (n=733), 30% to 39% (n=531), 20% to 29% (n=473), and <20% (n=271). Unadjusted rates for all-cause mortality in patients with RVEF >or=40%, 30% to 39%, 20% to 29%, and <20% were 27%, 32%, 35%, and 47%, respectively. When compared with patients with RVEF >or=40%, unadjusted hazard ratios and 95% confidence intervals for all-cause mortality for those with RVEF 30% to 39%, 20% to 29%, and <20% were 1.19 (0.97 to 1.46; P=0.087), 1.45 (1.17 to 1.78; P=0.001), and 1.98 (1.59 to 2.47; P<0.0001), respectively. Respective multivariable-adjusted hazard ratios (95% confidence intervals) for all-cause mortality associated with RVEF 30% to 39%, 20% to 29%, and <20% were 1.07 (0.87 to 1.32; P=0.518), 1.12 (0.89 to 1.40; P=0.328), and 1.32 (1.02 to 1.71; P=0.034), respectively. Adjusted hazard ratios (95% confidence intervals) for other outcomes associated with RVEF <20% (compared with >or=40%) were as follows: cardiovascular mortality, 1.33 (1.01 to 1.76; P=0.041); HF mortality, 1.61 (1.03 to 2.52; P=0.037); sudden cardiac death, 1.29 (0.87 to 1.91; P=0.212); all-cause hospitalization, 1.21 (1.00 to 1.47; P=0.056); and HF hospitalization, 1.39 (1.10 to 1.77; P=0.007). CONCLUSIONS: Baseline RVEF <20% is a significant independent predictor of mortality and HF hospitalization in systolic HF.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".