Temporal indication of cannabis use by means of THC glucuronide determination
Bibliographic record
Abstract
According to the regulations of the World Anti-Doping Agency (WADA), the use of cannabinoids is forbidden in competition. In doping controls, the detection of cannabinoid misuse is based on the analysis of the non-psychoactive metabolite 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (carboxy-THC). The determination of values greater than 15 ng/mL in urine represents an adverse analytical finding; however, no accurate prediction of the time of application is possible as the half-life of carboxy-THC ranges between three and four days. Consequently the detection of carboxy-THC in doping control urine samples collected in competition might also result from cannabis use in out-of-competition periods. The analysis of the glucuronide of the pharmacologically active delta 9-tetrahydrocannabinol (THC-gluc) may represent a complementary indicator for the detection of cannabis misuse in competition.An assay for the determination of THC-gluc in human urine was established. The sample preparation consisted of liquid-liquid extraction of urine specimens, and extracts were analysed by liquid chromatography/tandem mass spectrometry (LC-MS/MS). Authentic doping-control urine samples as well as specimens obtained from a controlled smoking study were analysed and assay characteristics such as specificity, detection limit (0.1 ng/mL), precision (>90%), recovery ( approximately 80%), and extraction efficiency (90%) were determined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".