Association Between Implementation of the Athlete Biological Passport and Female Elite Runners’ Performance
Bibliographic record
Abstract
The purpose of this research was to evaluate the performances of female middle- and long-distance runners before and after the implementation of a new antidoping strategy (the Athlete Biological Passport [ABP]) in a country accused of systematic doping. A retrospective analysis of the results of Russian National Championships from 2008 to 2017 was performed. The 8 best female performances for the 800-m, 1500-m, 3000-m steeplechase, 5000-m, and 10,000-m events from the semifinals and finals were analyzed. The yearly number of athletes fulfilling standard qualifications for international competitions was also evaluated. Overall, numbers of athletes banned for doping in 2008-2017 were calculated. As a result, 4 events (800, 1500, 5000 [all P < .001], and 10,000 m [P < .01]) out of 5 showed statistically significant deterioration in the performances when comparing before and after the introduction of the ABP. The 3000-m steeplechase was the only event that did not show statistically significant change. The highest relative decrease in the number of runners who met standard qualification for international competition was for the 5000-m event (46%), followed by 1500-m (42%), 800-m (38%), 10,000-m (17%), and 3000-m steeplechase (1%). In conclusion, implementation of the ABP was followed by a significant reduction in the performance of female runners in a country accused of systematic doping. It can be reasonably speculated that more stringent antidoping testing, more specifically the introduction of the ABP, is a key reason for this reduction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".