Assessing serum albumin concentration following exercise‐induced fluid shifts in the context of the athlete biological passport
Bibliographic record
Abstract
PURPOSE: The hydration status of an athlete at the time of a doping control sample collection is an important factor to consider when reviewing athlete biological passports (ABPs). Dehydration results in a reduction of the circulating plasma volume (PV), which may lead to artificially high values of some blood parameters. This study aimed to identify whether serum albumin could serve as a single marker of fluid shifts, which are not currently accounted for in the hematological passport. An additional marker could be used to assist experts when interpreting irregularities in the ABP. METHODS: Twelve subjects underwent multiple controlled exercise trials designed to induce varying levels of PV shifts. Pre-exercise blood samples were collected to establish baseline values for individual passports. During exercise interventions, blood samples were collected before the start of exercise and at 10 minute, 1 hour, 2 hours, and 24 hours following exercise. RESULTS: Significant increases in hematological parameters - hemoglobin [Hb], hematocrit (HCT), albumin (ALB), and calculated OFF-score - were identified at varying time points following fluid shift-inducing exercise. Changes in ALB correlated strongly with changes in [Hb] (r = 0.753) and with estimated PV shifts (r = -0.764). In analyzing ABPs, the resulting increases in Hb did not trigger any atypical findings at 99% specificity. PERSPECTIVE: Monitoring changes in ALB longitudinally may assist experts when reviewing PV shifts in the biological passport.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".