Abstract 19178: Cardiac Rehabilitation in the Elderly: An Under Referred Population That Does Not Attend
Bibliographic record
Abstract
Introduction: Elderly subjects have been shown in recent years to derive benefit from attending cardiac rehabilitation (CR). However, they are less likely to attend. Further, the dependency of CR referral on patient age has not been explored. Methods: The Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) database was used to obtain information on all patients. Subjects with at least one vessel CAD were considered as the pool of subjects to be referred. Patients were categorized according to age, <50 years, 50-60 years, 60-70 years, 70-80 years, and over 80 years of age. Rates of referral to and subsequent attendance at CR were compared across age categories. Logistic regression models were constructed to assess whether age predicted referral or attendance. Results: A total of 25,958 (24.6% female) subjects were included; of those, 3,266 (12.6%) were under 50 years of age, 6,330 (24.4%) were 50-60, 7,618 (29.4%) were 60-70, 6,798 (26.2%) were 70-80, and 1946 (7.5%) were over 80 years of age. Subjects in the higher age ranges had a greater prevalence of congestive heart failure and chronic obstructive pulmonary disease but lower prevalence of diabetes than their younger counterparts (all p<0.0001). Advanced age was associated with both decreased attendance and referral, though the association was stronger with referral (both p<0.0001, Figure 1). Relative to the youngest group, each age category had reduced referral in unadjusted and adjusted models (adjusted OR (95%CI): age 50-60: 0.86 (0.79, 0.95); age 60-70: 0.57 (0.52, 0.63); age 70-80: 0.34 (0.31, 0.38); age>80: 0.15 (0.13, 0.17)). Only the two oldest groups were less likely to attend. Conclusions: Advanced age represents a barrier to CR in two ways: one, elderly subjects are less likely to be referred, and subsequently less likely to attend. As elderly subjects have been shown to derive similar benefits from CR as their younger counterparts, age as a barrier to CR needs to be addressed.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".