Opioid Use in Athletes: A Systematic Review
Bibliographic record
Abstract
CONTEXT: The opioid epidemic has been well-documented in the general population, but the literature pertaining to opioid use and misuse in the athletic population remains limited. OBJECTIVES: The objectives of this study were to seek answers to the following questions: (1) what are the rates of opioid use and misuse among athletes, (2) do these rates differ compared with the nonathletic population, and (3) are there specific subgroups of the athletic population (eg, based on sport, level of play) who may be at higher risk? DATA SOURCES: The Embase, MEDLINE, and PubMed were used for the literature search. STUDY SELECTION: Records were screened in duplicate for studies reporting rates of opioid use among athletes. All study designs were included. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: Data regarding rates of opioid use, medication types, prescription patterns, and predictors of future opioid use were collected. Study quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) criteria for clinical studies and 5 key domains previously identified for survey studies. RESULTS: A total of 11 studies were eligible for inclusion (N = 226,256 athletes). Studies included survey studies and retrospective observational designs. Opioid use among professional athletes at any given time, as reported in 2 different studies, ranged from 4.4% to 4.7%, while opioid use over a National Football League career was 52%. High school athletes had lifetime opioid use rates of 28% to 46%. Risk factors associated with opioid use included Caucasian race, contact sports (hockey, football, wrestling), postretirement unemployment, and undiagnosed concussion. Use of opioids while playing predicted use of opioids in retirement. CONCLUSION: Overall, opioid use is prevalent among athletes, and use during a playing career predicts postretirement use. This issue exists even at the high school level, with similar rates to professional athletes. Further higher quality observational studies are needed to better define patterns of opioid use in athletes.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".