Association of Patients’ Familiarity and Perceptions of Efficacy and Risks With the Use of Opioid Medications in the Management of Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: While opioids are known to cause unintended adverse effects, they are being utilized by a number of patients with osteoarthritis (OA). The aim of this study was to evaluate the association of patient familiarity and perceptions regarding efficacy and risks with opioid medication use for OA. METHODS: A total of 362 adults with knee and/or hip OA were surveyed in this cross-sectional study. Patients' familiarity with and perceptions of benefits/risks of opioid medications were measured to evaluate potential associations with the utilization of opioid medications for OA within the last 6 months. Logistic regression models were adjusted for sociodemographic and clinical variables. RESULTS: In this sample, 28.7% (100/349) reported use of an opioid medication for OA-related symptoms in the last 6 months. Those who were on an opioid medication, compared to those who were not, were younger (mean age 62.5 vs 64.8 yrs), were more likely to have a high school education or lower (48.0% vs 35.3%), and had higher mean depression (Patient Health Questionnaire [PHQ]-8 7.2 vs 4.9) and OA-related pain (Western Ontario and McMaster Universities Arthritis Index [WOMAC] 54.8 vs 46.8) scores. After adjustment for sociodemographic and clinical variables, the following were associated with opioid medication use: higher perception of medication benefit (OR 1.68, 95% CI 1.18-2.41), lower perception of medication risk (OR 0.67, 95% CI 0.51-0.88), and having family or friends who received the medication for OA (OR 3.88, 95% CI 1.88-8.02). CONCLUSION: Among adults with knee/hip OA, opioid use was associated with being familiar with the treatment, as well as believing that the medication was beneficial and low-risk.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".