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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".