Monitoring Urinary Excretion of Cannabinoids by Fluorescence-Polarization Immunoassay: A Cannabinoid-to-Creatinine Ratio Study
Bibliographic record
Abstract
Drug testing in substance abuse treatment programs is focused on urine analysis of parent drugs and major metabolites. Huestis reported that serial monitoring of the major urinary cannabinoid metabolite (delta9-THC-COOH)-to-creatinine ratios in paired urine specimens (collected at least 24 hours apart) could differentiate new marijuana or hashish use from residual cannabinoid metabolite excretion in urine after previous drug use. Subjects with a history of chronic marijuana use were screened for cannabinoids in urine over several months by an enzyme immunoassay (EMIT) with a cut-off value of 50 ng/mL. Presumptive positive specimens were confirmed by gas chromatography-mass spectrometry (GC-MS) for delta9-THC-COOH with a cut-off value of 15 ng/mL. The objective of this study was to determine whether a semiquantitative cannabinoids immunoassay (corrected for creatinine concentration) could differentiate new marijuana use from residual cannabinoid excretion in chronic users of marijuana or hashish compared with GC-MS. The criterion for new marijuana use was a cannabinoid-to-creatinine ratio > or =0.5 (dividing the immunoassay quantitative result to creatinine ratio of specimen 2 by the specimen 1 ratio, specimen 3 by the specimen 2 ratio, etc.). Urine specimens were analyzed by fluorescence-polarization immunoassay (FPIA) on an Abbott TDxFLx analyzer after analysis by GC-MS. In 90 urine specimens (group A) with delta9-THC-COOH values determined by GC-MS, the mean delta9-THC-COOH concentration was 44.4 ng/mL (range, 16-100), and the mean FPIA total cannabinoids value was 91.7 ng/mL (range, 21-204 ng/mL) with a correlation coefficient of 0.993 (group A). In 111 specimens (group B), the mean delta9-THC-COOH concentration was 361 ng/mL (range, 101-960 ng/mL). The mean FPIA value was 657 ng/mL (range, 211-1,270 ng/mL), and the correlation coefficient of the B series was 0.975. Percent cross-reactivity for delta9-THC-COOH standards prepared in drug-free urine by FPIA was 82% at 25 ng/mL, 45% at 50 ng/mL, and 50% at 100 ng/mL. Overall, there was 89% agreement (132 of 148 specimens) between FPIA and GC-MS. In 16 of 148 specimens, however, the FPIA and GC-MS paired urine data did not agree. The sensitivity of the FPIA assay was 95.3%, and the specificity was 44.4%. The authors conclude that FPIA cannabinoid analysis should be further evaluated as an alternative to GC-MS quantitation to help distinguish new marijuana use from residual marijuana metabolite excretion in clinical drug treatment programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".