ABPS: An R Package for Calculating the Abnormal Blood Profile Score
Bibliographic record
Abstract
The Abnormal Blood Profile Score (ABPS) is used to identify blood doping in sport. It combines seven hematological markers, including hemoglobin level, reticulocytes percent, and haematocrit level, using two different machine learning algorithms in order to create a single score that has a better ability to identify doping than each parameter taken alone. The resulting score allows the detection of several types of doping using a single score and is part of the current Athlete Biological Passport program managed by World Anti-Doping Agency (WADA). We describe ≪ ABPS ≫, an R package that allows the calculation of this score. This is the first software implementation calculating this score that is released publicly. The package also contains functions to calculate the OFF-score (another score used for detection of doping), as well as several test datasets. The package is useful for laboratories conducting anti-doping analyses and for researchers working on anti-doping research projects. In particular, it has been successfully used in projects estimating the prevalence of blood doping.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.053 |
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".