Evidence-Based Recommendations on the Pharmacological Management of Osteoarthritis and Chronic Low Back Pain: An Asian Consensus
Bibliographic record
Abstract
The overall burden of chronic musculoskeletal pain in Asian countries will continue to increase as the population ages, as will the demand for safe and effective pain management. Currently available Asian guidelines are mostly outdated and targeted only to primary care. Implementation of international guidelines may be unsuitable for Asian patients due to cultural, local economic and regulatory factors. With the aim of developing Asian-specifi c consensus recommendations for the pharmacological management of osteoarthritis (OA) pain and chronic low back pain (cLBP), we convened to review and discuss recent available evidence for pharmacotherapy, clinical experiences, and current practice challenges they face in the region, including challenges in opioid use. Taking these into consideration, we provided general recommendations for the overall assessment and management of OA pain and cLBP. The strength of the recommendations regarding the use of pharmacological agents was assessed using the Grades of Recommendation Assessment, Development and Evaluation (GRADE) system. Where evidence is confl icting or limited, we made no recommendation pending the availability of further evidence. We recommend topical non-steroidal anti-infl ammatory drugs (NSAIDs) as a fi rst-line pharmacological treatment of OA pain, while oral NSAIDs should be considered as a fi rst-line pharmacological treatment of cLBP. Acetaminophen has been commonly used as the fi rst-line treatment for OA pain and cLBP, but its long-term use is not recommended based on recent evidence. These consensus recommendations are not prescriptive, and serve as a guide for decision-making in clinical practice. The optimal management of OA pain and cLBP should ultimately be individualized to each patient.
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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 | 0.041 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".