Rapid Communications
Bibliographic record
Abstract
Background and Aims: Bipolar patients who use cannabis have poorer compliance to treatment, more psychotic symptoms and a worse prognosis than patients who do not.In this study, we evaluated the importance of cannabis use among bipolar patients admitted to the Psychiatric Hospital of the Cross and clinical differences between users and non-users.Methods: Over 13 months, we recruited patients admitted for bipolar disorder.Patients were screened for substance abuse/dependence and were divided into two groups: cannabis users and non-users.We used the following questionnaires: MINI DSM-IV, the Young Mania Rating Scale (YMRS), the Montgomery and Åsberg Depression Rating Scale (MADRS), the Scale for the Assessment of Positive Symptoms (SAPS), and the Cannabis Abuse Screening Test (CAST).Results: Hundred patients were included.27% were cannabis users.Cannabis users were younger (33.6 vs 43.0 y.o.), more commonly male (77.8% vs 49.3%), and symptomatic at a younger age (24.6 vs 30.8 years old) of non-users.They had more hospital admissions in total (6.0 vs 3.7), and per year (0.73 vs 0.44) and higher socioeconomical state.Users presented more often for mania (59.3%) than in depression (11.1%).Among cannabis users, 55.6% and 33.3% represent the respective percentages of cannabis abuse and dependence.The mean CAST score in these patients was 13.4. Conclusions:In our sample, cannabis use was also associated with an earlier onset of the bipolar disorder and with higher number of hospitalizations.The age at the diagnosis of the bipolar disorder was 6.2 years lower among cannabis users.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.836 | 0.741 |
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".