Cardiac Rehabilitation
Bibliographic record
Abstract
In Brief PURPOSE: Despite well-documented positive benefits, cardiac rehabilitation (CR) is an underutilized resource for patients following a cardiac event or intervention. Bias in the CR referral process has led to programs designed to ensure that all eligible patients receive a referral. The purpose of the current investigation was to describe the implementation of a nurse-delivered automatic bedside referral process and to examine the effectiveness on referral and intake rates for CR. METHODS: In 2007, an automatic CR referral system was implemented at the University of Ottawa Heart Institute. A nurse-delivered automatic bedside referral process was implemented in 2008. A CR nurse screened all inpatient charts, discussed CR benefits and program options with patients, triaged the patient to the appropriate program, and facilitated booking of the CR intake appointment. Data were analyzed to determine the effectiveness of this approach. RESULTS: Only 15.5% to 19.7% of eligible patients participated in CR program prior to 2006. Implementation of an automatic referral process increased participation to 26.7%. The nurse-delivered bedside automatic referral process increased participation to 32.6%. The proportion of patients receiving CR referrals almost tripled following the implementation of the nurse-delivered referral process from 26.7% in 2003 to 79.0% in 2008. CONCLUSIONS: A nurse-delivered automatic bedside referral process had a positive impact on both referral and intake to CR. Future challenges for CR programs will be to ensure optimal participation in programs, while managing the growth associated with increased rates of involvement. The purpose of this study was to describe the implementation of a nursedelivered automatic bedside referral process for cardiac rehabilitation (CR). Following implementation, the proportion of patients receiving CR referrals almost tripled, while intake nearly doubled. A nurse-delivered automatic bedside referral process positively impacted both referral and intake to CR.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.020 |
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".