Evaluation of the detection of the homologous transfusion of a red blood cell concentrate in vivo for antidoping
Bibliographic record
Abstract
Two doping cases of homologous blood transfusion (HBT) during Tokyo 2020 Summer Olympics have shown that more controls are needed. The method of detection using flow cytometry to evaluate the expression of minor blood group antigens from red blood cells (RBCs) and identify different RBC populations is efficient but still complex to perform with multiple antigens detection. Recently, the interest of using forensic DNA analysis was also highlighted as a potential new method to detect HBT, with possibility to start from dried blood spots (DBS) instead of fresh blood. After a first phase of development, a protocol was validated for HBT detection using DNA analysis after extraction from DBS. Presence of a second DNA was clear down to 2% of donor blood in vitro. A flow cytometry protocol was also developed with preparation and analysis in 96-well plates and detection of two different antigens per well using two secondary antibodies with distinct fluorophores. The objective of the project was to evaluate the window of detection of an HBT performed in vivo with 150 mL of RBC concentrate. Blood samples obtained over 7 weeks post-transfusion were analyzed. DNA profiling from DBS was not sensitive enough to detect the presence of a second DNA even 1 day after transfusion. On the contrary, the flow cytometry protocol was very efficient and allowed identification of several double populations of RBC (expressing/non-expressing several antigens) until day 50 post-transfusion. This protocol can be fully validated for a future application to doping control samples.
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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 | 0.000 |
| 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".