Antidoping 2.0: Is Adding Power-Output Data to the Antidoping Pool the Next Step? Experts’ Viewpoint
Bibliographic record
Abstract
BACKGROUND: Efforts are needed to improve antidoping procedures. The widespread use of power meters among cyclists could help in this regard. However, controversy exists on whether performance monitoring through power-output data could be of help for antidoping purposes. PURPOSE: The objective of the present study was to provide insight into the feasibility and utility of implementing power-based performance monitoring in elite cycling. An expert panel of 15 applied sport scientists and professional cycling coaches were asked for their opinions and perspectives on incorporating power data into the antidoping risk-assessment process. RESULTS: Two different viewpoints were identified from the responses provided by the experts. Some believed that power monitoring could be implemented as an antidoping tool, provided that several surmountable challenges are first addressed. These authors provided suggestions related to the potential practical implementation of such measures. Others, on the contrary, believed that power meters lack sufficient reliability and suggest that the professional cycling world presents conflicts of interest that make this intervention impossible to implement nowadays. CONCLUSIONS: The debate around the utility of power-meter data in the antidoping fight has been ongoing for more than a decade. According to the opinions provided by the experts' panel, there is still no consensus on the real utility and practical implementation of this intervention.
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 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 | 0.049 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".