Are medical comorbidities contributing to the use of opioid analgesics in patients with knee osteoarthritis?
Bibliographic record
Abstract
BACKGROUND: Although opioid analgesics are not generally recommended for treatment of knee osteoarthritis (OA), they are frequently used. We sought to determine the association between medical comorbidities and self-reported opioid analgesic use in these patients. METHODS: This cross-sectional study recruited patients referred to two provincial hip and knee clinics in Alberta, Canada for consideration of total knee arthroplasty. Standardized questionnaires assessed demographic (age, gender, income, education, social support, smoking status) and clinical (pain, function, total number of troublesome joints) characteristics, comorbid medical conditions, and non-surgical OA management participants had ever used or were currently using. Multivariable Poisson regression with robust estimate of the standard errors assessed the association between comorbid medical conditions and current opioid use, controlling for potential confounders. RESULTS: 2,127 patients were included: mean age 65.4 (SD 9.1) years and 59.2% female. Currently used treatments for knee OA were: 57.6% exercise and/or physiotherapy, 61.1% NSAIDs, and 29.8% opioid analgesics. In multivariable regression, controlling for potential confounders, comorbid hypertension (RR 1.18, 95% CI 1.02-1.37), gastrointestinal disease (RR 1.31, 95% CI 1.07-1.60), depressed mood (RR 1.25, 95% CI 1.05-1.48) and a higher number of troublesome joints (RR 1.04 per joint, 95% CI 1.00-1.09) were associated with opioid use, with no association found with having ever used recommended non-opioid pharmacological or non-pharmacological treatments. CONCLUSIONS: In a large cohort of patients with knee OA, of 12 comorbidities assessed, comorbid hypertension, gastrointestinal disease, and depressed mood were associated with current use of opioid analgesics, in addition to total burden of troublesome joints. Improved guidance on the management of painful OA in the setting of common comorbidities is warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".