Subtyping negative symptoms in first-episode psychosis: Contrasting persistent negative symptoms with a data-driven approach
Bibliographic record
Abstract
Persistent negative symptoms (PNS) are linked to poor functional outcomes and may be primary or caused by secondary factors. Although several studies have examined PNS in first-episode psychosis (FEP), a comparison with a data-driven approach is lacking. Here, we compared clinically defined PNS subgroups with class trajectories identified through latent growth modeling (LGM). Patients admitted to an early intervention service (N = 392) were classified as PNS (n = 105), secondary PNS (sPNS; n = 74), or non-PNS (n = 213) based on longitudinal data collected six to twelve months after admission. LGM was used to stratify patients based on similar negative symptom course over the same time period. Using multiple linear regression, we assessed the utility of both approaches in predicting Social and Occupational Functioning Assessment Scale (SOFAS) scores at two-year follow-up. Three negative symptom trajectories were identified: low and remitting (LR; n = 158), moderate and improving (MI; n = 163) and delayed partial response (DR; n = 71). Most non-PNS patients followed the LR trajectory, while patients with PNS or sPNS were generally divided between MI and DR. Both PNS classification and trajectory membership were significant predictors of two-year functional outcomes; the DR and MI trajectories predicted greater increases in SOFAS scores (DR: b = -19.14; MI: b = -11.54) than either sPNS (b = -9.19) or PNS (b = -6.46). These findings demonstrate that combining PNS and symptom-based stratification can predict functional outcomes more accurately than either taxonomy alone. Such a combined approach could yield significant advances in developing more targeted interventions for patients at risk for poor functional outcomes.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".