Cannabis use and related clinical variables in patients with obsessive-compulsive disorder
Bibliographic record
Abstract
OBJECTIVE: Limited studies have investigated cannabis use in patients with obsessive-compulsive disorder (OCD), despite its widespread use by patients with psychiatric illnesses. The aim of this study was to assess the frequency, correlates, and clinical impact of cannabis use in an Italian sample of patients with OCD. METHODS: Seventy consecutive outpatients with OCD were recruited from a tertiary specialized clinic. To assess cannabis-related variables, patients completed a questionnaire developed for the purpose of this study, investigating cannabis use-related habits and the influence of cannabis use on OCD symptoms and treatments. A set of clinician and self-reported questionnaires was administered to measure disease severity. The sample was then divided into three subgroups according to the pattern of cannabis use: "current users" (CUs), "past-users" (PUs), and "non-users" (NUs). RESULTS: Approximately 42.8% of patients reported lifetime cannabis use and 14.3% reported current use. Approximately 10% of cannabis users reported an improvement in OCD symptoms secondary to cannabis use, while 23.3% reported an exacerbation of anxiety symptoms. CUs showed specific unfavorable clinical variables compared to PUs and NUs: a significant higher rate of lifetime use of tobacco, alcohol, and other substances, and a higher rate of pre-OCD onset comorbidities. Conversely, the three subgroups showed a similar severity of illness. CONCLUSION: A considerable subgroup of patients with OCD showed a predisposition towards cannabis use and was associated with some specific clinical characteristics, suggesting the need for targeted consideration and interventions in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".