ESC Study Group of Sports Cardiology Position Paper on adverse cardiovascular effects of doping in athletes
Bibliographic record
Abstract
The use of doping substances and methods is extensive not only among elite athletes, but also among amateur and recreational athletes. Many types of drugs are used by athletes to enhance performance, to reduce anxiety, to increase muscle mass, to reduce weight or to mask the use of other drugs during testing. However, the abuse of doping substances and methods has been associated with the occurrence of numerous health side-effects. The adverse effects depend on the type of the consumed drug, as well as the amount and duration of intake and the sensitivity of the body, since there is a large inter-individual variability in responses to a drug. Usually the doses used in sports are much higher than those used for therapeutic purposes and the use of several drugs in combination is frequent, leading to higher risk of side-effects. Among biomedical side-effects of doping, the cardiovascular ones are the most deleterious. Myocardial infarction, hyperlipidemia, hypertension, thrombosis, arrythmogenesis, heart failure and sudden cardiac death have been noted following drug abuse. This paper reviews the literature on the adverse cardiovascular effects after abuse of prohibited substances and methods in athletes, aiming to inform physicians, trainers and athletes and to discourage individuals from using drugs during sports.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".