Multidisciplinary Guidelines for the Rational Use of Topical Non-Steroidal Anti-Inflammatory Drugs for Musculoskeletal Pain (2022)
Bibliographic record
Abstract
(1) Background: Topical non-steroidal anti-inflammatory drugs (NSAIDs) are one of the primary drugs for treating musculoskeletal pain. However, there are currently no evidence-based recommendations about drug selection, drug administration, drug interactions, and use in special populations or other pharmacology-related content of such medications. To this end, the Chinese Pharmaceutical Association Hospital Pharmacy Professional Committee developed multidisciplinary guidelines on using topical NSAIDs to treat musculoskeletal pain. (2) Methods: The guidelines development process followed the World Health Organization guideline development handbook, the GRADE methodology, and the statement of Reporting Items for Practice Guidelines in Healthcare. The guideline panel used the Delphi method to identify six clinical questions to be addressed in the guidelines. An independent systematic review team conducted a systematic search and integration of evidence. (3) Results: Based on the balance between the benefits and harms of an intervention, the quality of the evidence, patient preferences and values, and resource utilization, the guideline panel developed 11 recommendations and nine expert consensuses on using topical NSAIDs to treat acute and chronic musculoskeletal pain. (4) Conclusions: Based on the effectiveness and overall safety of topical NSAIDs, we recommend patients with musculoskeletal pain use topical NSAIDs and suggest high-risk patients use topical NSAIDs, such as those with other diseases or receiving other concurrent treatments. The evidenced-based guidelines on topical NSAIDs for musculoskeletal pain incorporated a pharmacist perspective. The guidelines have the potential to facilitate the rational use of topical NSAIDs. The guideline panel will monitor the relevant evidence and update the recommendations accordingly.
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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 | 0.031 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".