A Synopsis of the Adverse Analytical and Atypical Findings Between 2005 and 2011 from the Doping Control Laboratory of Athens in Greece
Bibliographic record
Abstract
This article concerns the analysis of the Adverse Analytical Findings (AAFs) and the appropriate alterations made during the period 2005-2011, so that the Doping Control Laboratory of Athens (DCLA) obeys the updated World Anti-Doping Agency (WADA) List of Prohibited Substances. The % AAFs of the DCLA was compared with those of WADA-Accredited Laboratories. In 2008, the term Atypical Finding was introduced by the WADA representing a reported but inconclusive result. A characteristic example is when a testosterone-to-epitestosterone ratio is >4 followed by a negative gas chromatography/combustion/isotope ratio mass spectrometry result. In a total of about 30,000 athlete samples, 136 athletes were found with an increased testosterone/epitestosterone ratio and 43 with tetrahydrocannabinol metabolite (THCCOOH) of 427 reported AAFs. Twenty-one athletes in total were found positive with methylhexaneamine, the 11 found after a batch of 1000 samples was reprocessed. Besides, there were AAFs below their Minimum Required Performance Level (MRPL). The increasing need for higher detectability imposed new apparatus, e.g., liquid chromatography/quadrupole/time-of-flight mass spectrometry, whereas that for lowering the capital costs and reporting times led to the unification of the screening method which includes stimulants, diuretics, anabolics and other substances.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".