Comparison of cardiac rehabilitation outcomes in individuals with respiratory, cardiac or no comorbidities: A retrospective review.
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence and impact of respiratory comorbidities on patients undergoing cardiac rehabilitation (CR). METHODS: A retrospective review of a CR database (1999 to 2004) of patients with ischemic heart disease with ≥10 pack per year (ppy) smoking history and respiratory comorbidities (RC), non-respiratory comorbidities (NRC) and no comorbidities (NC) was performed. Primary outcomes at zero, six and 12 months included peak oxygen uptake (VO2peak), maximum workload, resting heart rate, ventilatory anaerobic threshold and anthropometrics. Analyses were performed on individuals who completed the program, adjusting for age, sex and baseline VO2peak. RESULTS: Of 5922 patients, 1247 had ≥10 ppy smoking history: 77 (6.2%) had RC; 957 (76.7%) had NRC; and 213 (17.1%) had NC. The program completion rate for each group was similar for the RC (46.8%), NRC (55.8%) and NC groups (57.3%) (P=0.26). The RC group had the lowest baseline fitness levels (P<0.002). For VO2peak, there were significant differences among groups (P=0.02) and improvements over program duration (P<0.0001). There were no significant differences in other outcomes. CONCLUSIONS: There was a low prevalence of patients with comorbid chronic obstructive pulmonary disease in CR when based on physician referral documentation. This is likely underestimated and/or reflects a referral bias. Diagnostic testing at CR entry would provide a more accurate measure of the prevalence and severity of disease. CR participation resulted in significant and similar improvements in most key CR outcomes in all groups including similar completion rate. A CR model was effective for patients with coexisting RCs. Strategies to improve access and diagnosis should be explored.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".