Factors Influencing Pain Management of Patients with Osteoarthritis: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is a complex disease associated with chronic pain. Many patients treat their joint pain at a symptomatic level with over-the-counter (OTC) pain medications, often without the knowledge of their physicians. The aim of this study was to provide physicians with data about osteoarthritic patients' habits of pain management and to examine the explanatory factors of various ways of self-treatment. METHODS: A cross-sectional study involving 189 patients with hip or knee OA and scheduled for joint replacement surgery was carried out. Participants filled out a self-administered questionnaire consisting of the Western Ontario and McMaster Universities Osteoarthritis Index and questions about their methods of alleviating pain. RESULTS: 2.6% of patients did not use anything to alleviate their pain, while 63% practiced a non-pharmacological method. Diclofenac was the most frequently used drug, followed by ibuprofen. Profession had the greatest impact on medication habits; patients doing manual work were significantly more likely to take OTC non-steroidal anti-inflammatory drugs and use topical analgesics. CONCLUSIONS: Patients utilized a wide variety of pain management techniques. They seemed to use well-known painkillers, even if their side effects were less desirable. Such patients require comprehensive pain management, including educational and behavioural interventions, complemented by topical and oral medication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".