Detecting autologous blood transfusions: a comparison of three passport approaches and four blood markers
Bibliographic record
Abstract
Blood passport has been suggested as an indirect tool to detect various kinds of blood manipulations. Autologous blood transfusions are currently undetectable, and the objective of this study was to examine the sensitivities of different blood markers and blood passport approaches in order to determine the best approach to detect autologous blood transfusions. Twenty-nine subjects were transfused with either one (n=8) or three (n=21) bags of autologous blood. Hemoglobin concentration ([Hb]), percentage of reticulocytes (%ret) and hemoglobin mass (Hbmass) were measured 1 day before reinfusion and six times after reinfusion. The sensitivity and specificity of a novel marker, Hbmr (based on Hbmass and %ret), was evaluated together with [Hb], Hbmass and OFF-hr by different passport methods. Our novel Hbmr marker showed superior sensitivity in detecting the highest dosage of transfused blood, with OFF-hr showing equal or superior sensitivities at lower dosages. Hbmr and OFF-hr showed superior but equal sensitivities from 1 to 4 weeks after transfusion compared with [Hb] and Hbmass, with Hbmass being the only tenable prospect to detect acute transfusions. Because autologous blood transfusions can be an acute practice with blood withdrawal and reinfusion within a few days, Hbmass seems to be the only option for revealing this practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".