Developing an International Standard Set of Patient-Reported Outcome Measures for Psychotic Disorders
Bibliographic record
Abstract
OBJECTIVE: The objective of this project was to develop a set of patient-reported outcome measures for adolescents and adults who meet criteria for a psychotic disorder. METHODS: A research team and an international consensus working group, including service users, clinicians, and researchers, worked together in an iterative process by using a modified Delphi consensus technique that included videoconferencing calls, online surveys, and focus groups. The research team conducted systematic literature searches to identify outcomes, outcome measures, and risk adjustment factors. After identifying outcomes important to service users, the consensus working group selected outcome measures, risk adjustment factors, and the final set of outcome measures. International stakeholder groups consisting of >100 professionals and service users reviewed and commented on the final set. RESULTS: The consensus working group identified four outcome domains: symptoms, recovery, functioning, and treatment. The domains encompassed 14 outcomes of importance to service users. The research team identified 131 measures from the literature. The consensus working group selected nine measures in an outcome set that takes approximately 35 minutes to complete. CONCLUSIONS: A set of patient-reported outcome measures for use in routine clinical practice was identified. The set is free to service users, is available in at least two languages, and reflects outcomes important to users. Clinicians can use the set to improve clinical decision making, and administrators and researchers can use it to learn from comparing program outcomes.
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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 | 0.127 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".