Quality and Variability of Cardiovascular Rehabilitation Delivery
Bibliographic record
Abstract
PURPOSE: Cardiac care, including cardiovascular rehabilitation (CR), is most effective if it is high-quality. The aim of this study was to describe CR quality, using the recently developed Canadian Cardiovascular Society CR quality indicators (QIs). Difference in quality between CR sites was also assessed. METHODS: Secondary analysis was conducted on an observational, prospective, multisite CR program evaluation cohort. A convenience sample of patients from 1 of 3 CR programs was approached at their first CR visit, and consenting participants completed a survey. Clinical data were extracted from charts pre- and postprogram. Of the 30 CR QIs, 21 (70.0%) were assessable: 10 process, 9 outcome, and 2 structure QIs. RESULTS: Of 411 consenting patients, 209 (53.0%) completed CR. The greatest quality was observed for assessment of blood pressure (98.1%), communication with primary health care at CR discharge (94.2%), and patient enrollment (94.0%). The lowest quality was observed for wait time from hospital discharge (9.2%), assessments of blood glucose (42.1%), and lipid control (53.0%). Of the 7 QIs that had an established benchmark, quality for 2 (28.6%) was above the benchmark (particularly assessment of blood pressure). Significant between-site differences were observed in 11 (64.7%) QIs. The magnitude of quality differences between sites was largest for assessment of lipid control (72.6%), assessment of blood glucose control (69.0%), and wait time in median days from referral to enrollment (30.6 days). CONCLUSION: There is wide variability in CR program quality, both overall and between CR sites. Quality improvement in particular aspects of CR care is required.
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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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".