SA52. The Prevalence of Negative Symptoms Across the Stages of the Psychosis Continuum
Bibliographic record
Abstract
Background: Negative symptoms are present in all stages of the psychosis continuum and still represent an unmet therapeutic need. A better understanding of their course over the lifetime has important implications for the development and refinement of timely interventions. While several studies have separately reported the prevalence rates of negative symptoms within each stage of the psychosis continuum, we sought to review the literature to compare the prevalence across stages to determine the course of such symptoms. Methods: Databases were searched for studies reporting prevalence rates of negative symptoms in one of our predetermined stages (i.e., clinical ultra-high risk—UHR, first-episode psychosis—FEP, younger (y) and older (o) patients who experienced multiple episodes of psychosis—MEP). Results were synthesized using negative symptoms’ definitions provided in a newly developed scale (Brief Negative Symptom Scale—BNSS). Prevalence rates of each negative symptom were averaged and weighted by the combined sample size. Results: Forty-seven studies were selected including 1872 UHR, 2947 FEP, 5039 yMEP, and 669 oMEP patients. The prevalence rates of each negative symptom followed a similar course; it first decreased between the UHR and the FEP stages and then reincreased in yMEP patients (anhedonia: FEP—26%, yMEP—57%; avolition: UHR—50%, FEP—28%, and yMEP—73%; asociality: UHR—49%, FEP—34%, and yMEP—48%; Blunted affect: UHR—21%, FEP—9%, yMEP—41%, oMEP—23%; Alogia: UHR—15%, FEP—7%, and yMEP—33%). Conclusion: The cumulative impact of negative symptoms might be influenced by certain psychological-, environmental-, and treatment-related factors. Interventions might benefit from prioritizing the overall most prevalent symptoms of avolition, asociality, and anhedonia.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".