Application of the heart failure meta-score to predict prognosis in patients with cardiac resynchronization defibrillators
Bibliographic record
Abstract
BACKGROUND: The Heart Failure (HF) Meta-score may be useful in predicting prognosis in patients with primary prevention cardiac resynchronization defibrillators (CRT-D) considering the competing risk of appropriate defibrillator shock versus mortality. METHODS: Data from 648 consecutive patients from two centers were used for the evaluation of the performance of the HF Meta-score. The primary endpoint was mortality and the secondary endpoint was time to first appropriate implantable cardioverter-defibrillator (ICD) shock or death without prior appropriate ICD shock. Fine-Gray model was used for competing risk regression analysis. RESULTS: In the entire cohort, 237 patients died over a median follow-up of 5.2 years. Five-year cumulative incidence of mortality ranged from 12% to 53%, for quintiles 1 through 5 of the HF Meta-score, respectively (log-rank P < 0.001). Compared with the lowest quintile, mortality risk was higher in the highest quintile (HR 6.9; 95%CI 3.7-12.8). The HF Meta-score had excellent calibration, accuracy, and good discrimination in predicting mortality (C-statistic 0.76 at 1-year and 0.71 at 5-year). The risk of death without appropriate ICD shock was higher in risk quintile 5 compared to quintile 1 (sub HR 5.8; 95%CI 3.1-11.0, P < 0.001). CONCLUSIONS: Our study demonstrated a good ability of the HF Meta-score to predict survival in HF patients treated with CRT-D as primary prevention. The HF Meta-score proved to be useful in identifying a subgroup with a significantly poor prognosis despite a CRT-D.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.034 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".