Evaluation of longitudinal <sup>13</sup>C isotope ratio mass spectrometric data in antidoping analysis
Bibliographic record
Abstract
The detection of testosterone and its precursors' abuse in antidoping sports analysis is based on the longitudinal evaluation of markers of the urinary endogenous steroid profile. A Bayesian statistical approach is applied, allowing the establishment of credible intervals of the selected parameters for every athlete. Samples showing values outside the acceptable boundaries are selected for additional confirmation by isotope ratio mass spectrometric (IRMS) analysis. The alterations of the IRMS values last longer than the alterations of the steroid profile. Then the application of IRMS to a larger number of samples, at a screening level, would presumably allow detection of additional positive cases. The steroid profile and IRMS data can be treated using the same Bayesian inference procedure. In nonsports population, we have demonstrated the stability of IRMS data. In this work, we studied the variability of these data in real conditions, in samples collected on athletes subjected to antidoping analyses over the years. The data obtained confirmed previous observations and the applicability of the proposed approach. The results of cases where confounding factors of the steroid profile were reported are discussed, showing that in most of the cases no significant changes are observed over the absolute delta values. Changes in diet may significantly change the absolute delta values but not the ones relative to endogenous reference compounds. Finally, a case that could have been evaluated as normal with the current approach without a thorough review of the data was detected as positive by the proposed approach, demonstrating the benefit of its application.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".