Automated Referral to Cardiac Rehabilitation following Coronary Artery Bypass Grafting is associated with limited improvements in program completion: a large cohort study
Bibliographic record
Abstract
IntroductionCardiac Rehabilitation (CR) reduces residual cardiovascular risk among patients who have received coronary artery bypass grafting (CABG) surgery. However, many patients do not attend and some are never referred. It is unclear whether automated referral is associated with improved CR completion rates.
 Objectives and ApproachGeographically inclusive databases were linked to assess the impact of automated referral on rates of referral to and completion of CR post-CABG. Automated referral to CR post-CABG was instituted in Calgary on July 1, 2007. All subjects receiving CABG in Calgary between January 1, 1996 and March 31, 2016 were enrolled in the study. The Alberta Provincial Project for Outcome Assessment in Coronary Heart disease (APPROACH) database, TotalCardiology-Rehabilitation (TC-R) database, and provincial vital statistics were linked using the unique Provincial Health Number available for each patient. The association between CR referral, completion, and survival was assessed using proportional hazard models.
 ResultsThere were 28,100 patients referred to the CR program, of which 26,411 were linked to the APPROACH database for a 93.99\% linkage rate. After excluding patients who did not receive CABG, a total of 8,118 patients were identified as the study population [mean age 66.2 (SD 10.2) years, 18.9\% female] during the study period: 5,103 prior to implementation of automated referral, and 3,015 post-automation. Rates of referral increased from 39.5\% prior to automation to 75.0\% post-automation (p
 Conclusion/ImplicationsAutomated referral to CR is associated with increased referral rates but less dramatic increases in CR completion rates post-CABG. Given the significant improvement in survival associated with CR completion, further work is needed to improve CR referral, and more importantly, CR completion rates.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".