Supervised exercise barriers and readmission in patients with heart failure in a low-resource setting Barriers to Cardiac Rehabilitation in patients with heart failure
Bibliographic record
Abstract
Abstract Background: This study assessed cardiac rehabilitation (CR) barriers in heart failure (HF) patients for the first time, use of formal exercise supervision, and readmissions. Methods: This was a prospective study of HF in-patients at a private hospital. The CR Barriers Scale (CRBS), and International Physical Activity Questionnaire were administered. Participants were called 30 and 90-days to ascertain formal exercise supervision and readmissions. Results: Of 95 participants, 85 (89.5%) were retained at the 30-day call, and 86 (90.5%) at 90; 2 died. The mean total CRBS score was 2.3±.6.5, with highest item scores for lack of energy, already exercising, lack of awareness, distance and exercise pain/fatigue. Only 1 participant enrolled in CR, but close to half had engaged an exercise professional (n=48, 56.5% and n=45, 52.3%) at both follow-ups. 25.8% of patients were readmitted at 30 days and 25.5% at 90 days. Participants who had professional exercise supervision within 30 days had significantly fewer readmissions (n=7, 14.6%) compared with patients who did not (n=13, 35.1%; p=0.03). Conclusions: CR barriers are high in HF patients. Despite some accessing professional exercise training, most were insufficiently active. Systematic CR referral and coverage advocacy could mitigate this poor self-management, and ultimately the high readmissions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".