SU127. Negative Symptoms in Youth at Clinical High Risk of Psychosis
Bibliographic record
Abstract
Background: Longitudinal studies examining youth at clinical high risk (CHR) of psychosis have predominantly focused on positive symptoms. However, youth at CHR often demonstrate persistent and significant negative symptoms, which have been reported to be predictive of conversion to psychosis. The goal of this study was to examine negative symptoms over time in youth at CHR of psychosis and compare baseline negative symptoms in those who convert to psychosis with those who did not convert. Methods: Youth at CHR (N = 764) were recruited for the North American Prodrome Longitudinal Study (NAPLS 2) at 8 sites across North America. Negative symptoms were rated on the Scale of Prodromal Symptoms (SOPS) at baseline, 6, 12, 18, and 24 months. Difference in prevalence of negative symptoms was assessed using Z test and change in negative symptom severity over time was assessed using repeated measures analysis of variance ANOVA. Wilcoxon rank sum test and 2-sample t test were utilized to compare baseline negative symptoms in converters vs nonconverters. Results: The mean total negative symptom score at baseline was 11.90 (SD = 9.80). A majority of participants (84.57%) had at least one negative symptom rated ≥3 at baseline. Negative symptom severity significantly decreased over time compared to baseline measures. Eighty-six participants converted in total. In participants with at least one negative symptom of moderate severity or above (N ≥ 3), nonconverters had lower severity ratings on expression of emotion (M = 1.49, SD = 1.47 vs M = 1.94, SD = 1.64, P = .02) and ideational richness (M = 1.23, SD = 1.37 vs M = 1.60, SD = 1.35, P = .04) compared to converters at baseline. In participants who completed 24 months of assessment and had negative symptom severity of moderate severity or above (N ≥ 3), nonconverters had significantly better expression of emotion (M = 1.40, SD = 1.51) compared to converters (M = 1.79, SD = 1.63, P = .03). Conclusion: First, this study demonstrated that the majority of youth at CHR have moderate to severe negative symptoms at baseline. Second, both decreased expression of emotion and decreased ideational richness was significantly more severe in participants who converted and may be indicative of later conversion to psychosis. Thus, early and persistent higher negative symptom scores may represent subsequent risk of conversion to psychosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".