DropWise: current role and future perspectives of dried blood spots (DBS), blood microsampling, and their analysis in sports drug testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it