Screening for Autologous Blood Transfusions
Bibliographic record
Abstract
The ratio between the amount of hemoglobin in the mature erythrocyte population and the reticulocytes (RBCHb:RetHb ratio) has previously been suggested as a marker to screen for EPO-abuse. We speculated that the reinfusion of blood would lead to a marked increase in this ratio, making it a valuable parameter in the screening for autologous blood doping. Three bags of blood (approximately 201+/-11 g of Hb) were withdrawn from 16 males and stored at either -80 degrees C (-80 T, n=8) or +4 degrees C (+4 T, n=8) and reinfused 10 weeks or 4 weeks later, respectively. Seven subjects served as controls. Different erythrocyte parameters were measured on a hematological analyzer serially throughout and during a 4 week wash-out period. By using RBCHb:RetHb ratio cut-off limits of 145.7 (1:100) ('suspicious') and 182.9 (1:1000) ('positive'), 35.4% (-80 T) and 19.6% (+4 T) of all samples obtained during a 4 week wash-out period were identified as 'suspicious', and 18.8% (-80 T) and 4.3% (+4 T) as 'positive'. In total, 7 out of 16 (43.8%) subjects had at least one sample exceeding 182.9. Compared to the currently used indirect parameters, the RBCHb:RetHb ratio is the best indicator of autologous blood doping after reinfusion, and the parameter could be used in a testing setting, once stability validation has been performed.
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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 | 0.001 | 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) | 0.002 | 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".