Interventions and Transition in Youth at Risk of Psychosis
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this systematic review and meta-analyses was to summarize the impact of all reported treatments on transition to psychosis in high-risk samples. DATA SOURCES: PsycINFO, Embase, CINAHL, EBM, and MEDLINE online databases were searched from inception to May 2017 using the keywords psychosis, risk, and treatment with no geographical, date, or language restrictions. STUDY SELECTION: A total of 38 independent studies met the inclusion criteria: conducted a treatment study in a sample at high risk for psychosis and reported on transition to psychosis as an outcome. DATA EXTRACTION: The following data were extracted: study characteristics (eg, sample size), participant characteristics (eg, mean age), and clinical outcome data (eg, number and percentage of patients transited for each intervention group at each time-point and transition assessment employed). Data were analyzed using random-effects pairwise meta-analysis (to explore differences between treatment and controls) and multivariate network meta-analyses (NMAs; to explore differences between treatment types on transition) and were reported as risk ratios (RR). RESULTS: In pairwise meta-analyses, cognitive-behavioral therapy (CBT) studies were associated with a significant reduction in transition compared with controls at 12-month and 18-month follow-up (RR = 0.57; 95% CI, 0.35-0.93; I² = 7%; P = .02 vs RR = 0.54; 95% CI, 0.32-0.92; I² = 0%; P = .02). In the NMAs, integrated psychological therapy, CBT, supportive therapy, family therapy, needs-based interventions, omega-3, risperidone plus CBT, ziprasidone, and olanzapine were not significantly more effective at reducing transition at 6 and 12 months relative to each other. CONCLUSIONS: This systematic review and pairwise meta-analyses demonstrated a reduced risk for transition favoring CBT at 12 and 18 months. No interventions were significantly more effective at reducing transition compared with all other interventions in the NMAs. NMA results should be interpreted with caution due to the small sample size.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".