Are Patients with Schizophrenia Reliably Reporting their Cannabis Use? An African Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Most studies of the prevalence of cannabis use among patients with schizophrenia used a self-report as declared by the patient himself. We hypothesize that patients with schizophrenia did not tell the truth and might underreport their use for many reasons to be discussed later. Indeed, the under-report of cannabis use among these patients can affect the effectiveness of their treatment. AIMS: To assess the degree of agreement between the prevalence values obtained from patients' reports and the results of the toxicological tests. METHODS: A cross-sectional study was carried out on 403 patients with schizophrenia. A sociodemographic, psychiatric history and illicit drug use profile was performed for each patient. We assessed the patients with the Positive and Negative Syndrome Scale (PANSS), Calgary Depression score (CDSS), Barratt Impulsiveness Score (BIS-10) and Medication Adherence Rating Scale (MARS). The consumption of cannabis used was confirmed with MINI International Neuropsychiatric Interview (MINI-DSM IV) and using toxicological analysis. RESULTS: Among the 403 patients who consented to give their urine samples, 49.1% (198/403) tested positive for cannabis, and 41.41% (82/198) underreported their use. The sensitivity and specificity of the questionnaire were 0.58 and 0.74. Based on the comparison between sociodemographic and psychiatric history data of patients who self-report and underreport their cannabis use, no significant difference was observed except for the duration of cannabis use and the score on the medication adherence scale. Moreover, it was found that impulsivity, PANSS score, CDSS score, and the type of schizophrenia are not involved in predicting the underreporting of cannabis use. CONCLUSION: The rate of patients who under-report cannabis use is important. Therefore, toxicological analysis is becoming relevant for identifying drug use among schizophrenic patients and in the addictive comorbidity research field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".