DropWise: current role and future perspectives of dried blood spots (DBS), blood microsampling, and their analysis in sports drug testing
Bibliographic record
Abstract
For decades, blood testing has been an integral part of routine doping controls. The breadth of information contained in blood samples has become considerably more accessible for anti-doping purposes over the last 10 years through technological advancements regarding analytical instrumentation as well as enhanced sample collection systems. Particularly, microsampling of whole blood and serum, for instance as dried blood spots (DBS), has opened new avenues in sports drug testing and substantially increased the availability and cost-effectiveness of doping control specimens. Thus, microvolume blood specimens possess the potential to improve monitoring of blood hormone and drug levels, support evaluation of circulating drug concentrations in competition, and enhance the stability of labile markers and target analytes in blood passport analyses as well as peptide hormone and steroid ester detection. Further, the availability of the fraction of lysed erythrocytes for anti-doping purposes warrants additional investigation, considering the sequestering capability of red blood cells (RBCs) for certain substances, as a complementary approach in support of the clean sport.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".