Psychopathology in Substance Use Disorder Patients with and without Substance-Induced Psychosis
Bibliographic record
Abstract
Background. Substance-induced psychotic disorder (SIPD) is a diagnosis constructed to distinguish substance-induced psychotic states from primary psychotic disorders. A number of studies have compared SIPD persons with primary psychotic patients, but there is little data on what differentiates substance use disorder (SUD) individuals with and without SIPD. Here, we compared psychopathology, sociodemographic variables, and substance use characteristics between SUD patients with and without SIPD. Methods. A retrospective chart review was conducted on newly admitted patients at a rehabilitation centre between 2007 and 2012. Results. Of the 379 patients included in the study, 5% were diagnosed with SIPD (n = 19) and 95% were diagnosed with SUDs without SIPD (n = 360). More SIPD patients reported using cannabis and psychostimulants, and fewer SIPD patients reported using alcohol than SUDs patients without SIPD. SIPD patients scored higher on the "schizophrenia nuclear symptoms" dimension of the SCL-90R psychoticism scale and exhibited more ClusterB personality traits than SUD patients without SIPD. Discussion. These data are consistent with previous studies suggesting that psychopathology, substance type, and sociodemographic variables play important role in the development of SIPD. More importantly, the results highlight the need for paying greater attention to the types of self-reported psychotic symptoms during the assessment of psychotomimetic effects associated with psychoactive substances.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".