Doping control analysis of <i>myo</i>‐inositol trispyrophosphate and 10 bisphosphonates in equine plasma by ion chromatography–mass spectrometry and its application to clodronic acid horse administration
Bibliographic record
Abstract
Bisphosphonates and myo-inositol trispyrophosphate (ITPP) are two classes of difficult-to-detect polar drugs that are prohibited under the rules of racing. ITPP is a drug capable of increasing the amount of oxygen in hypoxic tissues, and studies have shown that administration of ITPP increases the maximal exercise capacity in mice. The properties of ITPP make it an ideal candidate as a doping agent to enhance performance in racehorses. In recent years, ITPP had indeed been detected in racehorses and confiscated items. As for bisphosphonates, it is especially critical to control their use as since February 2019, the International Agreement on Breeding, Racing and Wagering (IABRW) by the International Federation of Horseracing Authorities (IFHA) had identified specific conditions on which bisphosphonates should not be administered to a racehorse. A recent review of literature shows that there is yet a simultaneous screening method for detecting ITPP and bisphosphonates in equine samples. This paper describes an efficient ion chromatography high-resolution mass spectrometry (IC-HRMS) method for the simultaneous detection of ITPP and 10 bisphosphonates at sub-parts-per-billion (ppb) to low-ppb levels in equine plasma after solid-phase extraction (SPE) and its application to an administration study of clodronic acid in horses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".