Direct quantification of 11‐nor‐Δ <sup>9</sup> ‐tetrahydrocannabinol‐9‐carboxylic acid in urine by liquid chromatography/tandem mass spectrometry in relation to doping control analysis
Bibliographic record
Abstract
An accurate and precise method for the quantification of 11-nor-Delta(9)-tetrahydrocannabinol-9-carboxylic acid (THCA) in urine by liquid chromatography/tandem mass spectrometry (LC/MS/MS) for doping analysis purposes has been developed. The method involves the use of only 200 microL of urine and the use of D(9)-THCA as internal standard. No extraction procedure is used. The urine samples are hydrolysed using sodium hydroxide and diluted with a mixture of methanol/glacial acetic acid (1:1). Chromatographic separation is achieved using a C8 column with gradient elution. All MS and MS/MS parameters were optimised in both positive and negative electrospray ionisation modes. For the identification and the quantification of THCA three product ions are monitored in both ionisation modes. The method is linear over the studied range (5-40 ng/mL), with satisfactory intra-and inter-assay precision, and the relative standard deviations (RSDs) are lower than 15%. Good accuracy is achieved with bias less than 10% at all levels tested. No significant matrix effects are observed. The selectivity and specificity are satisfactory, and no interferences are detected. The LC/MS/MS method was applied for the analysis of 48 real urine samples previously analysed with a routine gas chromatography/mass spectrometry (GC/MS) method. A good correlation between the two methods was obtained (r(2) > 0.98) with a slope close to 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".