Pain among Individuals with Chronic Respiratory Diseases Attending Pulmonary Rehabilitation
Bibliographic record
Abstract
Purpose: This study reports on the prevalence and impact of pain in individuals with different chronic respiratory diseases attending pulmonary rehabilitation (PR). Method: A retrospective review of medical records data was conducted for 488 participants who had attended a PR programme over a 2-year period. Data on pain and medication history taken from multidisciplinary medical records, together with participant demographics and PR outcomes, were extracted. We compared pain among participants with different types of chronic respiratory disease. Results: The overall prevalence of pain was 77%, with a significantly higher prevalence among individuals with obstructive lung diseases (80%) compared with restrictive lung diseases (69%; p = 0.04). Some participants (17%) who took pain medications did not discuss pain with their clinicians. The presence of pain and different reporting of pain did not have a negative impact on the PR programme completion rate ( p = 0.74), improvements in exercise capacity ( p = 0.51), or health-related quality of life (all four chronic respiratory disease questionnaire domains, p>0.05). Conclusions: The prevalence of pain is high among individuals with chronic respiratory disease attending PR. The presence or absence of pain was not negatively associated with the programme completion rate or PR outcomes; therefore, pain should not deter clinicians from referring patients to PR.
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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 | 0.001 | 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 | 0.000 |
| 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".