Mass spectrometry in sports drug testing: Structure characterization and analytical assays
Bibliographic record
Abstract
Owing to the sensitive, selective, and unambiguous nature of mass spectrometric analyses, chromatographic techniques interfaced to various kinds of mass spectrometers have become the most frequently employed strategy in the fight against doping. To obtain utmost confidence in analytical assays, mass spectrometric characterization of target analytes and typical dissociation pathways have been utilized as basis for the development of reliable and robust screening as well as confirmation procedures. Methods for qualitative and/or quantitative determinations of prohibited low and high molecular weight drugs have been established in doping control laboratories preferably employing gas or liquid chromatography combined with electron, chemical, or atmospheric pressure ionization followed by analyses using quadrupole, ion trap, linear ion trap, or hyphenated techniques. The versatility of modern mass spectrometers enable specific as well as comprehensive measurements allowing sports drug testing laboratories to determine the misuse of therapeutics such as anabolic-androgenic steroids, stimulants, masking agents or so-called designer drugs in athletes' blood or urine specimens, and a selection of recent developments is summarized in this review.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".