Rethinking the psychosis spectrum: A meta-analysis unveils a nonlinear discontinuity in verbal learning deficits between at-risk conditions and psychotic disorders
Bibliographic record
Abstract
Abstract Background Transition from a categorical to a dimensional approach has been proposed in the field of psychosis. However, whether key features of schizophrenia, such as cognitive deficits, really do lie along a linear continuum remains uncertain. To explore this, we compared for the first time verbal learning impairments in six entities of the psychosis spectrum using linear, nonlinear, and categorical models. Methods Studies involving verbal learning tests in familial high risk, clinical high risk, schizotypy/schizotypal, ultra-high risk, first episode of psychosis (FEP), and chronic schizophrenia populations were systematically searched in three databases in September 2024. Studies were included if they reported an immediate, delayed, or total recall measure in subclinical or clinical entities and healthy controls. The metafor package was used to compute effect sizes for the comparison between cases and control groups, categorized by psychosis entities. Model comparisons were also performed to compare linear, nonlinear, and categorical distributions of the effect sizes. Results The meta-analysis aggregated a total of 262 studies in the psychosis spectrum. Effect sizes were moderate in at-risk populations (<0.50) and large in clinical populations (−1.00 for FEP and >1.00 for chronic schizophrenia). A nonlinear model best explained our data in immediate recall, while the results in delayed and total recall suggest the inferiority of linear models. Conclusions Our findings suggest a discontinuity in verbal learning between at-risk populations and clinical entities, challenging a purely linear dimensional model of cognitive impairment in the psychosis spectrum.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.030 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".