Prevalence, frequency, adverse events, and reasons for analgesic use in youth athletes: A systematic review and meta-analysis of 44,381 athletes
Bibliographic record
Abstract
OBJECTIVES: To identify the prevalence, frequency, adverse effects, and reasons for analgesic use in youth athletes. DESIGN: Systematic review and meta-analysis. METHODS: Systematic searches in Embase, Medline, and SPORT-Discus from inception to September 2021, screening of reference lists, and citation tracking were performed to identify observational studies including athletes aged 15-24 years and reporting data on prevalence and/or frequency of analgesic use. Study quality was assessed using the Newcastle-Ottawa Scale. Random-effect proportion meta-analyses, stratified by type of analgesic medication and prevalence measure, estimated the prevalence of analgesic use. Data on usage frequency, adverse events, and reasons for analgesic use was synthesized narratively. RESULTS: Forty-nine studies were included (44,381 athletes), of which 19 were good/high quality. Seven categories of analgesics were identified across 10 prevalence time-points. Meta-analyses suggested common use of NSAIDs (point prevalence 48 % [95 % CI 23 % to 73 %], in-season prevalence 92 % [95 % CI 88 % to 95 %]). The lowest prevalence was found for use of local anesthetic injections within the previous 12 months (2 % [95 % CI 1 % to 3 %]). Seven to 50 % of athletes reported weekly analgesics use. The proportion of adverse events ranged from 3.3 % to 19.2 %. Reasons for using analgesics included treatment of sports-related pain or injury, to treat illness, and to enhance performance. CONCLUSIONS: Analgesics are commonly used in youth athletes, but estimates vary depending on type of analgesic and prevalence measure. As the majority of studies were of poor methodological quality, future high-quality research should include prospective data collection of analgesic use to understand consumption trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".