Approach to managing musculoskeletal pain: acetaminophen, cyclooxygenase-2 inhibitors, or traditional NSAIDs?
Bibliographic record
Abstract
OBJECTIVE: To provide family physicians and pharmacists with practical, evidence- and expertise-based guidance on choosing the safest approach to using analgesics to manage patients with musculoskeletal pain. SOURCES OF INFORMATION: Health care providers from family practice, rheumatology, gastroenterology, hepatology, internal medicine, and pharmacy participated in an educational needs assessment regarding the management of pain and the safety of commonly used analgesics. Feedback from one-on-one interviews was compiled and distributed to participants who selected key topics. Topics chosen formed the basis for the discussions of this multidisciplinary panel that reviewed data on the safety of analgesics, particularly in regard to comorbidity and concurrent use with other therapies. MAIN MESSAGE: Treatment should begin with an effective analgesic with the best safety profile at the lowest dose and escalate to higher doses and different analgesics as required. Acetaminophen is a safe medication that should be considered first-line therapy. Nonsteroidal anti-inflammatory drugs (NSAIDs) are associated with potential adverse gastrointestinal, renal, hepatic, and cardiovascular effects. Physicians should not prescribe NSAIDs before taking a careful history and doing a physical examination so they have the information they need to weigh the risks (adverse effects and potential drug interactions) and benefits for individual patients. CONCLUSION: Taking a complete and accurate history and doing a physical examination are essential for choosing the safest analgesic for a particular 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".