Trends in cardiac rehabilitation enrollment post-coronary artery bypass grafting upon implementation of automatic referral in Southeast Asia: A retrospective cohort study
Bibliographic record
Abstract
Introduction: Cardiac rehabilitation (CR) is an effective but underutilized intervention. Strategies have been identified to increase its use, but there is paucity of data testing them in low-resource settings. We sought to determine the effect of automatic referral post-coronary artery bypass graft (CABG) surgery on CR enrollment. Methods: This is a retrospective cohort study assessing cardiac patients referred to CR at a tertiary center in Southeast Asia from 2013 to 2019. The paper-based pathway was introduced at the end of 2012. The checklist with automatic CR referral on the third day post-operation prompted a nurse to educate the patient about CR, initiate phase 1 and encourage enrollment in phase 2. Patients who were not eligible for the pathway for administrative or clinical reasons were referred at the discretion of the attending physician (i.e., usual care). Enrollment was defined as attendance at≥1 CR visit. Results: Of 4792 patients referred during the study period, 394 enrolled in CR. Significantly more patients referred automatically enrolled compared to usual care (225 [11.8%] vs. 169 [5.8%]; OR=2.2, 95% CI=1.8-2.7), with increases up to 23.4% enrollment in 2014 (vs. average enrollment rate of 5.9% under usual referral). Patients who enrolled following automatic referral were significantly younger and more often employed (both P<0.001); no other differences were observed. Conclusion: In a lower-resource, Southeast Asian setting, automatic CR referral is associated with over two times greater enrollment in phase 2 CR, although efforts to maintain this effect are 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".