Influence of Crossover on Mortality in a Randomized Study of Revascularization in Patients With Systolic Heart Failure and Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: To assess the influence of therapy crossovers on treatment comparisons and mortality at 5 years in patients with ischemic heart disease and heart failure randomly assigned to medical therapy alone (MED) or to MED and coronary artery bypass graft (CABG) surgery in the Surgical Treatment for Ischemic Heart Failure (STICH) trial. METHODS AND RESULTS: The influence of early crossover (within the first year after randomization) on 5-year mortality was assessed using time-dependent multivariable Cox models. CABG was performed in 65/602 patients (10.8%) assigned to MED, and 55/610 patients (9.0%) assigned to CABG received MED only. Common reasons for crossover from MED to CABG were progressive symptoms or acute decompensation. MED-assigned patients who underwent CABG had lower 5-year mortality than those who received MED only (25% vs 42%; hazard ratio, 0.50; 95% confidence interval, 0.30-0.85; P=0.008).The main reason for crossover from CABG to MED was patient/family decision. Five patients did not undergo their assigned CABG within a year but died before receiving surgery without status change. They were deemed crossover to MED. The CABG-to-MED crossover population had higher 5-year mortality compared with those treated with CABG per-protocol (59% vs 33%; hazard ratio, 2.01; 95% confidence interval, 1.36-2.96; P<0.001). CABG was associated with lower mortality compared with MED in per-protocol and several time-dependent analyses (all P<0.05). CONCLUSIONS: CABG reduced mortality in both the per-protocol and crossover STICH patient populations. Crossover from assigned therapy, therefore, diminished the impact of CABG on survival in STICH when analyzed by intention to treat. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00023595.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".