Patient-Reported Outcome Measures in Clinical High Risk for Psychosis: A Systematic Review
Bibliographic record
Abstract
A key issue in both research and clinical work with youth at clinical high risk (CHR) of psychosis is that there are clearly heterogenous clinical outcomes in addition to the development of psychosis. Thus, it is important to capture the psychopathologic outcomes of the CHR group and develop a core outcomes assessment set that may help in dissecting the heterogeneity and aid progress toward new treatments. In assessing psychopathology and often poor social and role functioning, we may be missing the important perspectives of the CHR individuals themselves. It is important to consider the perspectives of youth at CHR by using patient-reported outcome measures (PROMs). This systematic review of PROMs in CHR was conducted based on a comprehensive search of several databases and followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Sixty-four publications were included in the review examining PROMs for symptoms, functioning, quality of life, self-perceptions, stress, and resilience. Typically, PROMs were not the primary focus of the studies reviewed. The PROMs summarized here fit with results published elsewhere in the literature based on interviewer measures. However, very few of the measures used were validated for CHR or for youth. There are several recommendations for determining a core set of PROMs for use with CHR.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".