The role of social acuity assessment in differentiating primary psychoses from drug-induced psychoses
Bibliographic record
Abstract
Introduction The dual diagnosis among patients with primary psychotic disorders is frequent and causes diagnostic and treatment challenges. In clinical practice, differentiating between substance-induced psychoses and independent (primary) psychoses when the patient is actively using drugs of addiction, is difficult, especially in the acute phase of the psychosis. Objectives The aim of the study is to identify clinical data relevant for differentiating between primary psychoses triggered by addictive drug misuse and substance-induced psychoses, using psychometric scales. Methods The study was conducted on 111 patients divided in four samples: 28 dual diagnosis psychotic patients (DD), 27 bipolar patients (BD), 25 schizoaffective patients (SCA) and 31 patients with schizophrenia (SCZ). The subjects were assessed using scales for the severity of psychiatric symptoms, cognitive functions and social acuity (theory of mind): BPRS-E (Brief Psychiatric Rating Scale – Expanded), MoCA (Montreal Cognitive Assessment), CBS (Cambridge Behavioral Scale), and RMET (Reading the Mind in the Eyes Test). The tests were performed when patients were in the remission phase of the psychosis. Results BPRS-E scores showed significant differences between DD subjects and patients from the other three samples (primary psychoses). CBS revealed significant differences between the DD subjects and patients with schizophrenia spectrum psychoses (SCA and SCZ). RMET identified significant differences between DD and BD patients. Conclusions Although differentiating between substance-induced and primary psychoses remains a difficult task, social acuity assessment performed in remitted patients may be helpful in guiding the clinician to establish a more accurate diagnosis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".