The Global Epidemiology of Anabolic Steroid Use.
Bibliographic record
Abstract
INTRODUCTION: The worldwide prevalence of anabolic-androgenic steroid (AAS) use is poorly documented, and geographical distribution of studies concerning AAS use is mostly limited to the USA, Canada, Brazil and some European countries. In addition, no quantitative meta-analysis has been conducted on the global prevalence rate of AAS use. METHODS: We performed the first ever meta-analysis and meta-regression analysis of AAS use using studies gathered from searches in PsycINFO, PubMed, ISI Web of Science, Google Scholar among others. Included were 187 studies that provided original data on 271 lifetime prevalence rates. Studies were coded for publication year, region, sample type, age range, sample size, assessment method, and sampling method. Heterogeneity was assessed by the I 2 index and the Q –statistic. Random effect-size modeling was used. Subgroup comparisons were conducted using Bonferroni correction. RESULTS: The global lifetime prevalence rate obtained was 3.3% (95 CI, 2.8–3.8, I 2 = 99.7, P < 0.001]. The prevalence rate for males, 6.4% (95% CI, 5.3–7.7, I 2 = 99.2, P < 0.001), was significantly higher ( Q bet = 100.1, P < 0.001) than the rate for females, 1.6% (95% CI, 1.3–1.9, I 2 = 96.8, P < 0.001). Sample type (athletes), assessment method (interviews only and interviews and questionnaires), sampling method, and male sample percentage were significant predictors of AAS use prevalence. There was no indication of publication bias.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".