The Prevalence of Negative Symptoms Across the Stages of the Psychosis Continuum
Bibliographic record
Abstract
BACKGROUND: Patients in every stage of the psychosis continuum can present with negative symptoms. While no treatment is currently available to address these symptoms, a more refined characterization of their course over the lifetime could help in elaborating interventions. Previous reports have separately investigated the prevalence of negative symptoms within each stage of the psychosis continuum. Our aim in this review is to compare those prevalences across stages, thereby disclosing the course of negative symptoms. METHODS: We searched several databases for studies reporting prevalences of negative symptoms in each one of our predetermined stages of the psychosis continuum: clinical or ultra-high risk (UHR), first-episode of psychosis (FEP), and younger and older patients who have experienced multiple episodes of psychosis (MEP). We combined results using the definitions of negative symptoms detailed in the Brief Negative Symptom Scale, a recently developed tool. For each negative symptom, we averaged and weighted by the combined sample size the prevalences of each negative symptom at each stage. RESULTS: We selected 47 studies totaling 1872 UHR, 2947 FEP, 5039 younger MEP, and 669 older MEP patients. For each negative symptom, the prevalences showed a comparable course. Each negative symptom decreased from the UHR to FEP stages and then increased from the FEP to MEP stages. CONCLUSIONS: Certain psychological, environmental, and treatment-related factors may influence the cumulative impact of negative symptoms, presenting the possibility for early intervention to improve the long-term course.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".