Pre-participation Withdrawal and Noncompletion of Cardiac Rehabilitation in Peripheral Artery Disease
Bibliographic record
Abstract
PURPOSE: Despite the mortality benefit of cardiac rehabilitation (CR) participation, as well as its cost-effectiveness for people with peripheral artery disease (PAD), there are limited data on adherence and completion of CR in those with and without concomitant coronary artery disease (CAD). The objective of this study was to compare CR pre-participation withdrawal and noncompletion between patients with PAD and concomitant PAD and CAD (PAD/CAD) versus matched and unmatched patients with CAD (uCAD). METHODS: Consecutively referred patients between 2006-2017 with PAD (n = 271) and PAD/CAD (n = 610) were matched to CAD by age, sex, diabetes, smoking status, and referral year. The uCAD (n = 14 487) group was included for comparison. Reasons for withdrawal were ascertained by interview. RESULTS: There were no significant differences in pre-participation withdrawal between PAD and matched CAD (46 vs 43%, P = .49), nor in noncompletion (22 vs 18%, P = .28). Results were similar for PAD/CAD and matched CAD (withdrawal: 36 vs 34%, P = .37) and (noncompletion: 25 vs 23%, P = .46). A smaller proportion of patients with uCAD withdrew (28%) than patients with PAD ( P < .001) and PAD/CAD ( P < .001), with no difference in noncompletion ( P > .40, both). There were no differences between PAD and PAD/CAD and their matched counterparts for medical and nonmedical reasons for withdrawal and noncompletion ( P ≥ .25, all). CONCLUSION: Pre-participation withdrawal rates were similar between patients with PAD, PAD/CAD, and their matched cohorts but greater than patients with uCAD. Once patients started CR, there were similar completion rates among all groups. Reports that patients with PAD are less likely to start CR may be related to their complex medical profile rather than PAD alone. Strategies to improve participation among patients with PAD should focus on the immediate post-referral period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".