Trends in referral to outpatient cardiac rehabilitation in the Hunter Region of Australia, 2002-2007
Bibliographic record
Abstract
BACKGROUND: Cardiac rehabilitation (CR) is an underutilized evidence-based treatment. We described trends in referral to outpatient CR (OCR) and the factors associated with referral. DESIGN: Cross-sectional survey data provided by Hunter residents aged 20 years or older discharged from public hospitals in the region between 2002 and 2007 with an OCR eligible diagnosis were extracted from the Hunter New England Heart and Stroke Register database. METHODS: Trends in referral were determined using the chi test for trend. Factors associated with referral were examined using multiple logistic regression. RESULTS: Sixty-five percent (4971 of 7678) of patients provided sufficient data for inclusion in the analysis. Approximately half of the patients reported being referred to OCR. No increase over time was observed. Factors associated with referral were age less than 70 years, male sex, being married, urban residence, at least one admission to the tertiary referral hospital for cardiology, at least one admission for acute myocardial infarction, revascularization, no admissions for congestive heart failure, a self-reported history of high cholesterol, and no history of stroke or atrial fibrillation. CONCLUSION: Access to this treatment of proven benefit remained suboptimal despite the provision of new programs and expansion of existing programs. Automatic referral, which is recommended in Australia, should be standard practice.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".