Randomized Laboratory Study of Single-Dose Cannabis, Dronabinol, and Placebo in Patients With Schizophrenia and Cannabis Use Disorder
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Up to 43% of people with schizophrenia have a lifetime cannabis use disorder (CUD). Tetrahydrocannabinol (THC) has been shown to exacerbate psychosis in a dose-dependent manner, but little research has assessed its effects on schizophrenia and co-occurring CUD (SCZ-CUD). In this double-dummy, placebo-controlled trial (total n = 130), we hypothesized that a modest dose of THC would worsen cognitive function but not psychosis. STUDY DESIGN: Effects of single-dose oral THC (15 mg dronabinol) or smoked 3.5% THC cigarettes vs placebo in SCZ-CUD or CUD-only on positive and negative symptoms of schizophrenia (only for SCZ-CUD), cognition, and drug experiences assessed several hours after drug administration. SCZ-only and healthy control participants were also assessed. STUDY RESULTS: Drug liking was higher in THC groups vs placebo. Neither smoked THC nor oral dronabinol predicted positive or negative symptom subscale scores 2 and 5 h, respectively, after drug exposure in SCZ-CUD participants. The oral dronabinol SCZ-CUD group, but not smoked THC SCZ-CUD group, performed worse than placebo on verbal learning (B = -9.89; 95% CI: -16.06, -3.18; P = .004) and attention (B = -0.61; 95% CI: -1.00, -0.23; P = .002). Every 10-point increment in serum THC + THCC ng/ml was associated with increased negative symptoms (0.40 points; 95% CI: 0.15, 0.65; P = .001; subscale ranges 7-49) and trends were observed for worse positive symptoms and performance in verbal learning, delayed recall, and working memory. CONCLUSIONS: In people with SCZ-CUD, a modest single dose of oral THC was associated with worse cognitive functioning without symptom exacerbation several hours after administration, and a THC dose-response effect was seen for negative symptoms.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".