Efficacy and safety of non-steroidal anti-inflammatory drugs (NSAIDs) for the treatment of acute pain after orthopedic trauma: a practice management guideline from the Eastern Association for the Surgery of Trauma and the Orthopedic Trauma Association
Bibliographic record
Abstract
Objectives: Fracture is a common injury after a traumatic event. The efficacy and safety of non-steroidal anti-inflammatory drugs (NSAIDs) to treat acute pain related to fractures is not well established. Methods: Clinically relevant questions were determined regarding NSAID use in the setting of trauma-induced fractures with clearly defined patient populations, interventions, comparisons and appropriately selected outcomes (PICO). These questions centered around efficacy (pain control, reduction in opioid use) and safety (non-union, kidney injury). A systematic review including literature search and meta-analysis was performed, and the quality of evidence was graded per the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. The working group reached consensus on the final evidence-based recommendations. Results: A total of 19 studies were identified for analysis. Not all outcomes identified as critically important were reported in all studies, and the outcome of pain control was too heterogenous to perform a meta-analysis. Nine studies reported on non-union (three randomized control trials), six of which reported no association with NSAIDs. The overall incidence of non-union in patients receiving NSAIDs compared with patients not receiving NSAIDs was 2.99% and 2.19% (p=0.04), respectively. Of studies reporting on pain control and reduction of opioids, the use of NSAIDs reduced pain and the need for opioids after traumatic fracture. One study reported on the outcome of acute kidney injury and found no association with NSAID use. Conclusions: In patients with traumatic fractures, NSAIDs appear to reduce post-trauma pain, reduce the need for opioids and have a small effect on non-union. We conditionally recommend the use of NSAIDs in patients suffering from traumatic fractures as the benefit appears to outweigh the small potential risks.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".