Canadian Guidelines for the Assessment and Diagnosis of Patients with Schizophrenia Spectrum and Other Psychotic Disorders
Bibliographic record
Abstract
OBJECTIVE: The objective of this article is to identify best practices in the diagnosis and assessment of patients with schizophrenia spectrum and other psychotic disorders. The diagnosis and assessment may occur in a range of situations from the emergency room to the outpatient clinic and at different stages of the disorder. The focus may be on acute exacerbations of illness, residual symptoms, levels of function, or changes in the response to treatment. METHODS: A systematic search was conducted for guidelines published in the last 5 years for schizophrenia and schizophrenia spectrum disorders. The guidelines were rated by at least 2 raters, and recommendations adopted on the diagnosis and assessment were primarily drawn from the American Psychiatric Association practice guidelines for the psychiatric evaluation of adults and the National Institute for Health and Care Excellence guideline on psychosis and schizophrenia in adults. A number of de novo recommendations were also developed. RESULTS: Eleven recommendations were identified that cover a range of assessment situations from diagnosis to the involvement of families in assessments. CONCLUSIONS: An accurate assessment establishes the baseline for treatment planning based on clinical decision making for both pharmacotherapy and psychosocial treatments.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".