Prevalence and risk factors of musculoskeletal pain in patients with chronic obstructive pulmonary disease: A systematic review
Bibliographic record
Abstract
OBJECTIVES: This systematic review aimed to discuss the prevalence and the risk factors of the musculoskeletal pain in chronic obstructive pulmonary disease (COPD). DATA SOURCE AND STUDY SELECTION: Four databases were analysed (Scopus, PubMed, Cochrane and EMBASE). We excluded systematic reviews, meta-analyses, conference abstracts and case reports. Two authors independently checked for the eligibility of the relevant articles. The risk of bias was evaluated using the Newcastle Ottawa Quality Assessment Scale and the Joanna Briggs Institute critical appraisal checklist. The selection and evaluation of studies followed the PRISMA guidelines. RESULTS: Twenty studies were retrieved, including from 21 to 7952 patients with COPD. The prevalence of pain was highly heterogeneous across studies: 7-89.7%. Pain was mostly reported in the lumbar (7-69%) and cervical spine (11-48.3%) and the chest (44-82.8%). The main risk factors for developing pain were old age, sex (female), level of physical activity (low) and comorbidities. CONCLUSION: Pain is a very common symptom in patients with COPD. Despite this, few clinical trials have investigated the pain. It appears to be located primarily in the lumbar, cervical and thoracic regions and facilitated by being a female, a low level of physical activity, comorbidity(ies) and old age.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".