Trends in the Detection of Erythropoietin Receptor Agonists (ERAs) in Anti‐Doping: An Analysis of Recent Adverse Analytical Findings (AAFs)
Bibliographic record
Abstract
Anti-doping efforts aim to reduce the prevalence of doping through a combination of education, deterrence, and detection. Detection of doping practices, for example through testing and/or investigations, aims both to catch committed dopers and deter potential dopers. To date, little empirical evidence is available examining the ability of detection strategies to deter athletes from doping. Here, trends in adverse analytical findings (AAFs) for EPO or other EPO-Receptor Agonists (ERAs) were examined over an 8-year period in order to assess the impact of ERA testing and detection on athlete behavior. It was observed that the majority (62.8%) of ERA AAFs occur on samples collected on the day of a competition. Evidence is also presented that the largest fraction of ERA AAFs occurs on the first sample ever taken from an athlete (43.2%), and that the ERA AAF rates decline steadily as athletes continue to be tested. These findings provide evidence of a deterrent effect of testing on ERA use in sport.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.019 |
| 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".