Heterogeneity in assessing the risk of developing a psychotic disorder given a previous subclinical psychotic-like experience in children and adolescents: A systematic review and meta-analysis
Bibliographic record
Abstract
• Child and adolescent PLEs increase the risk of psychosis onset by young adulthood. • A third of psychotic disorder diagnoses are attributed to child or adolescent PLEs. • Interview-based assessments of PLEs better identify psychosis prone trajectories. • Further work is needed to improve and standardize the assessment of PLEs. Psychotic-like experiences (PLEs) are common in the general population. Child and adolescent PLEs are the most prevalent and linked with future psychotic disorders. Significant heterogeneity in PLE assessment has obscured its clinical utility to identify psychosis-prone trajectories and improve clinical outcomes. This meta-analysis aimed to assess i) PLE prevalence in children and adolescents and ii) their relationship with subsequent psychotic disorder while exploring sources of heterogeneity. PubMed, Embase, PsycINFO, and CINAHL databases were searched in August 2023 for population-based longitudinal studies that assessed child or adolescent PLEs and early adulthood psychotic outcomes. Six studies were included ( n = 16,560), showing a pooled PLE prevalence of 17.3 %. Child and adolescent PLEs were associated with an increased risk of psychotic disorder in early adulthood (unadjusted OR = 3.80, 95 % CI: 2.31–6.26), with a population attributable fraction of 32.6 %. Significant heterogeneity in the strength of this relationship ( I 2 = 70 %, p = .01) was related to assessment type (self-report vs. interview). This review contends that interview-based PLE assessments could more accurately identify children or adolescents on a path towards psychosis and are better suited for psychotic risk identification. Further research is needed to elucidate interactions between PLEs and other psychotic risk factors.
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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 | 0.028 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.055 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".