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Record W3197577913 · doi:10.1111/crj.13443

Prevalence and risk factors of musculoskeletal pain in patients with chronic obstructive pulmonary disease: A systematic review

2021· review· en· W3197577913 on OpenAlexaboutno aff
Fabien Latiers, Marie Vandenabeele, William Poncin, Grégory Reychler

Bibliographic record

VenueThe Clinical Respiratory Journal · 2021
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCOPDPhysical therapySystematic reviewChecklistMeta-analysisLumbarMEDLINELow back painComorbidityInternal medicineAlternative medicineSurgeryPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.385
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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