FAMP7 A New Clinical Care Paradigm Improves Cardiac Rehabilitation Enrollment In Post Myocardial Infarction Patients: Targeting the Care Gap following Hospital Discharge
Bibliographic record
Abstract
Initiation of secondary prevention programming (i.e. cardiac rehabilitation) among myocardial infarction (MI) survivors significantly reduces subsequent disease-related events. Unfortunately, less than half of these individuals participate in cardiac rehabilitation (CR). Barriers, such as non-standardized CR referral processes, delayed timing of program delivery, and communication gaps between health care settings, in accessing CR programming are cited as primary factors for suboptimal participation rates in this form of secondary prevention. Purpose: To develop and implement a clinical model that will augment the rate of MI survivors successful transitioning between acute inpatient care and outpatient CR services. Methods: A construct of the cardiac patient journey was developed through a collaboration with acute and community based cardiac care settings by triangulating information from three principle data sources: 1) a patient needs assessment survey collected as part of an early follow-up clinical pilot project in a ST elevation myocardial infarction (STEMI) population; 2) a process review of hospital based CR referral and discharge teaching practices; 3) a process review of CR based referral and patient program scheduling activities. Information collected from this four-point analysis was then used to develop a health services transition care model.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".