Selection for psychosocial treatment for youth at clinical high risk for psychosis based on the North American Prodrome Longitudinal Study individualized risk calculator
Bibliographic record
Abstract
AIM: Recent findings suggest that family-focused therapy (FFT) is effective for individuals at clinical high-risk for psychosis (CHR-P). As outcomes of CHR-P individuals are quite varied, certain psychosocial interventions may be differentially effective in subgroups. The present study examined change in positive symptoms for CHR-P individuals at different levels of predicted risk for conversion to psychosis who received either FFT, a brief form of family education termed enhanced care (EC) or treatment as usual. METHODS: Participants were drawn from the North American Prodromal Longitudinal Study (NAPLS2). A subset of NAPLS2 participants completed a randomized study involving FFT or EC. The present study includes participants from the FFT-CHR sub-study and non-randomized NAPLS2 participants. Predicted risk of conversion was calculated using the Individualized Risk Calculator for Psychosis. Robust linear regressions evaluated whether the association between predicted risk of conversion and positive symptom change differed across intervention groups. RESULTS: A total of 94 participants from the FFT-CHR sub-study (FFT-CHR n = 50, EC n = 44) and 401 non-randomized NAPLS2 participants were included in this study. There was a treatment group by predicted risk of conversion interaction that predicted positive symptom improvement: higher risk individuals improved more with FFT-CHR than EC or the non-randomized NAPLS group, whereas lower-risk individuals did not differ in positive symptom improvement across treatment groups (FFT-CHR vs EC: P = .03, β = 20.27; FFT-CHR vs NAPLS2: P < .001, β = 28.40). CONCLUSIONS: Intensive treatments such as FFT-CHR may be most appropriate for individuals at the highest levels of clinical risk for psychosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".