Fast IRMS screening of pseudoendogenous steroids in doping analyses
Bibliographic record
Abstract
The detection of the abuse of pseudoendogenous steroids (testosterone and/or its precursors) is currently based, when possible, on the application of the steroid module of the World Anti-Doping Agency (WADA), athlete biological passport (ABP), implemented through the global database, ADAMS. When a suspicious sample is detected, the confirmation by isotope ratio mass spectrometry (IRMS) is required. It is well known that this confirmation procedure is time consuming and expensive and can be only applied on a reduced number of samples. In previous studies we have demonstrated that the longitudinal evaluation of the IRMS data is able to detect positive samples that otherwise will be evaluated as negative, improving the efficacy of the fight against doping in sport. This would require the analysis of a much larger volume of samples by IRMS. The aim of the present work is to describe an IRMS screening method allowing to increase the throughput of samples that can be analyzed by IRMS. The detection efficacy of the method is compared with the confirmation method in use, and to assess its robustness and applicability, all the samples of a major cycling stage competition were analyzed, with the agreement of the testing authority, under routine conditions and response times. The results obtained permit to conclude that the IRMS screening method here proposed has adequate selectivity and produces results that overlap with the already validated method currently in use permitting to analyze a much higher volume of samples even during a major event without compromising the detection capacity. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".