Assessing the Concordance Between Self-Reported Cannabis Use and Urine Toxicology in Canadian Youth and Young Adults Attending an Early Psychosis Programme
Bibliographic record
Abstract
Background: Youth and young adults with early psychosis frequently use cannabis, yet the reliability of self-reported use is uncertain in clinical practice. We examined the concordance between self-reported cannabis use and urine toxicology among patients enrolled in an Early Psychosis Intervention (EPI) program in Southeast Ontario, Canada. Methods: We conducted a cross-sectional chart review of 116 EPI patients (2016–2019). Demographics, self-reported cannabis use (yes/no), concurrent substance use, and urine toxicology results from the initial clinical assessment were extracted. Diagnostic indices (sensitivity, specificity, positive/negative predictive values, and accuracy) were calculated using urine toxicology as the reference. The clinical panel used a 50 ng/mL threshold for THC-COOH; the specific assay platform (immunoassay vs. confirmatory GC-/LC-MS) was not specified in records and is noted as a limitation. Results: Overall, 82.8% (96/116) self-reported cannabis use. Self-report showed high sensitivity (88.4%) but very low specificity (20.3%), with PPV 39.2%, NPV 75.0%, and accuracy 45.30%, indicating limited concordance with urine toxicology. Self-reported cannabis use was significantly associated with self-reported cocaine and MDMA use, while associations with methamphetamine, opioids, and benzodiazepines were not significant. Conclusions: In this EPI cohort, self-reports overestimated cannabis use relative to urine toxicology (high sensitivity, low specificity, and accuracy <50%). These findings support cautious clinical interpretation of self-report and the complementary value of biological verification, especially when use is infrequent or the testing window/threshold may miss exposure. Future work should incorporate use frequency, potency, and timing relative to testing, and clearly specify toxicology assay methods.
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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".