Effect of Glucocorticoids on Athletic Performance: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of glucocorticoids in enhancing athletic performance. DESIGN: At least 2 independent reviewers conducted study selection and extracted demographic and outcome data. Relevant outcomes were stratified by administration time frame and the specific type of drug used. Study quality was assessed using the Cochrane Risk-of-Bias tool and the Cochrane Grading of Recommendations Assessment Development and Education scale. Where appropriate, meta-analyses were performed. Data sources: Embase, MEDLINE, and SPORTDiscus were searched from their beginning to April 2020. PARTICIPANTS: Participants of any sex and training status aged 18 to 65 years were included. INTERVENTIONS AND MAIN OUTCOME MEASURES: Any type of published randomized controlled trial (RCT) that examined any enhancement in sport as well as aerobic, anaerobic, or body compositional parameters for glucocorticoids compared with placebo. RESULTS: There is low-to-moderate evidence suggesting that the administration of glucocorticoids may be more beneficial than placebo in enhancing athletic performance. short-term administration of glucocorticoids significantly improved time to exhaustion, maximal force, and total distance travelled. By contrast, acute administration of glucocorticoids predominantly yielded no changes to athletic performance, except for reductions in total work and maximal power output. CONCLUSIONS: Although there is evidence suggesting glucocorticoids have ergogenic effects, these improvements may differ depending on the specific type of drug, dose, and the administration time frame and are also limited by small sample sizes. Therefore, there is a need for large, high-quality RCTs as this may influence future doping policy and athlete care.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".