A multi‐parametric approach to remove the influence of plasma volume on the athlete biological passport during a Union Cycliste Internationale cycling stage race
Bibliographic record
Abstract
Fluctuations in plasma volume (PV) present potential confounders within the concentration-based markers of the haematological athlete biological passport (ABP). Here, a multi-parametric approach involving a simple blood test is applied to the current ABP adaptive model in an attempt to remove the influence of PV expansion, induced by a cycling stage race. Blood samples were obtained from 29 professional cyclists (14 male, 15 female) before, during and after 4-5 consecutive days of racing. Whole blood was analysed in accordance with the World Anti-Doping Agency ABP guidelines for haemoglobin ([Hb]) concentration and platelets. Serum and plasma were analysed for transferrin, albumin, calcium, creatinine, total protein and low-density lipoprotein. PV variation (Z-scores) was estimated using a multi-parametric model (consisting of the biomarkers mentioned earlier) and compared against calculated variations in PV (measured via CO-rebreathing). Significant reductions in [Hb] and the OFF-score were observed in female cyclists after 3 and 4 days of racing, with accompanying increases in PV, which returned to baseline values 4 days post competition. Similarly, a significant increase in PV was observed in male cyclists after 3 and 5 days of racing. When individual estimations of PV variance were applied to the adaptive model, the upper and lower reference predictions for [Hb] and the OFF-score were refined such that all outliers consistent with racing-induced PV changes were removed. The PV model appears capable of reducing the influence of PV on concentration-dependent markers during competition. This is an important step towards the inclusion of the PV correction in the ABP haematological module.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".