Pulmonary Rehabilitation in Chronic Obstructive Pulmonary Disease: Predictors of Program Completion and Success
Bibliographic record
Abstract
Although participation in pulmonary rehabilitation (PR) improves the health outcomes in patients with Chronic Obstructive Pulmonary Disease (COPD), there are insufficient resources to provide PR to all patients with COPD. Thus, predicting which patients are at risk for drop-out and non-response to rehabilitation is necessary in order to optimize limited resources. This study examined which patient characteristics are predictive of PR drop-out and non-response. 814 patients with COPD took part in standard out-patient PR for 8 weeks. Demographic and standard clinical data were collected before the rehabilitation program had started. Data was analyzed retrospectively to determine if baseline patient characteristics could predict drop-out and non-response to rehabilitation. Drop-out was defined as participation in less than 50% of the rehabilitation sessions. Non-response was defined as improvement less than 4% on the St. George's Respiratory Questionnaire (SGRQ). A discriminant function analysis identified age, smoking history, and health status as predictors of patient drop-out, p < .0001, with younger, current smokers and patients with lower health status being at risk for drop-out. No variables measured significantly predicted who those at risk would be for non-response to rehabilitation, p > .05. Pulmonary function data did not predict drop-out or non-response to PR. These findings indicate that perceived impairment (i.e., health status) is more likely to influence completion of rehabilitation than actual pulmonary impairment and that demographic and standard clinical data do not adequately predict patient drop-out and non-response to rehabilitation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".