Abnormal Hematologic Profiles in Elite Cross-Country Skiers: Blood Doping or?
Bibliographic record
Abstract
OBJECTIVE: There is widespread public concern about fairness in sports. Blood doping undermines fairness and places athletes' health at risk. The purpose of this study was to examine the prevalence of abnormal hematologic profiles in elite cross-country skiers, which may indicate a high probability of blood doping. SETTING AND PARTICIPANTS: Samples were obtained as part of routine International Ski Federation blood testing procedures from participants at the World Ski Championships. Sixty-eight percent of all skiers and 92% of those finishing in the top 10 places were tested. MAIN OUTCOME MEASURES: Using flow cytometry, we analyzed erythrocyte and reticulocyte indices. Reference values were from the 1989 Nordic Ski World Championships data set and the International Olympic Committee Erythropoietin 2000 project. RESULTS: Of the skiers tested and finishing within the top 50 places in the competitions, 17% had "highly abnormal" hematologic profiles, 19% had "abnormal" values, and 64% were normal. Fifty percent of medal winners and 33% of those finishing from 4th to 10th place had highly abnormal hematologic profiles. In contrast, only 3% of skiers finishing from 41st to 50th place had highly abnormal values. CONCLUSIONS: These data suggest that blood doping is both prevalent and effective in cross-country ski racing, and current testing programs for blood doping are ineffective. It is unlikely that blood doping is less common in other endurance sports. Ramifications of doping affect not only elite athletes who may feel compelled to risk their health but also the general population, particularly young people.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".