Verbal Memory Performance and Completion of Cardiac Rehabilitation in Patients With Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVE: To assess cognitive performance as a predictor of noncompletion of cardiac rehabilitation (CR) using a standardized verbal memory test. METHODS: This was a prospective cohort study of consecutive patients with coronary artery disease (n = 131) entering 1-year outpatient CR between April 2007 and May 2009. Verbal memory performance was assessed using the California Verbal Learning Test, Second Edition. Attendance at weekly CR sessions was recorded, and completion or noncompletion was determined according to comprehensive CR criteria. Depression was diagnosed according to DSM-IV criteria as a possible confounder. RESULTS: Verbal memory performance at entry into CR differed significantly (F(1,130) = 7.80, p = .006) between noncompleters and completers (mean [SD] cumulative California Verbal Learning Test, Second Edition, score, -1.15 [2.59] versus 0.47 [3.12]) in analysis of covariance controlling for pertinent clinical confounders. Better verbal memory performance predicted a reduced risk of noncompletion (hazard ratio [HR] = 0.86, 95% confidence interval [CI] = 0.77-0.96, p = .009) in time-to-event analysis adjusted for depression (HR = 2.62, 95% CI = 1.33-5.17, p = .006) and smoking history (HR = 2.03, 95% CI = 0.98-4.22, p = .06). A post hoc analysis suggested that better verbal memory performance predicted a reduced risk of noncompletion for medical reasons (HR = 0.83, 95% CI = 0.70-0.99, p = .03). CONCLUSIONS: Poorer verbal memory performance was associated with an increased risk of noncompletion of CR among participants with coronary artery disease. Further studies exploring practical methods for screening and targeted support might improve rehabilitation 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".