Efficacy of topical versus oral analgesic medication compared to a placebo in injured athletes: A systematic review with meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Athletes are injured frequently and often take analgesic medication. Moreover, athletes commonly use non-prescription topical and oral medications with little guidance. Despite wide use, relatively few studies exist on the efficacy of pain medication in injured athletes compared to a placebo. OBJECTIVE: To determine efficacy of topical or oral medications in pain reduction compared to a placebo in injured athletes. STUDY DESIGN: A systematic review and meta-analysis. METHODS: We conducted an electronic search using Medline/Pubmed, Web of Science, Ovid, and SportDiscus for all literature relating to topical or oral medications in athletes for pain management post-injury. Two reviewers screened the studies and measured their quality. To determine efficacy, we calculated the Hedges' g value. We created forest plots with 95% CI to graphically summarize the meta-analyses. RESULTS: There was a significant pooled effect size reflecting a reduction in pain outcomes for the topical treatment versus placebo (g = -0.64; 95% CI [-0.89, -0.39]; p < 0.001). There was not a significant reduction in pain outcomes for the oral treatment versus placebo (g = -0.26; 95% CI [-0.60, 0.17]; p = 0.272). CONCLUSION: Topical medications were significantly better at reducing pain compared to oral medications versus a placebo in injured athletes. These results are different when compared to other studies that used experimentally induced pain versus musculoskeletal injuries. The results from our study suggest that athletes should use topical medications for pain reduction, as it is more effective, and there are less reported adverse effects compared to 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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".