Identifying persistent negative symptoms in first episode psychosis
Bibliographic record
Abstract
BACKGROUND: Although persistent negative symptoms (PNS) are known to contribute significantly to poor functional outcome, they remain poorly understood. We examined the heuristic value of various PNS definitions and their respective prevalence in patients with first episode psychosis (FEP). We also contrasted those definitions to the Proxy for the Deficit Syndrome (PDS) to identify deficit syndrome (DS) in the same FEP cohort. METHODS: One hundred and fifty-eight FEP patients were separated into PNS and non-PNS groups based on ratings from the Scale for Assessment of Negative Symptoms (SANS). PNS was defined in the following ways: 1) having a score of 3 or greater on at least 1 global subscale of the SANS (PNS_1); 2) having a score of 3 or more on at least 2 global subscales of the SANS (PNS_2); and 3) having a score of 3 or more on a combination of specific SANS subscales and items (PNS_H). For all three definitions, symptoms had to be present for a minimum of six consecutive months. Negative symptoms were measured upon entry to the program and subsequently at 1,2,3,6,9 and 12 months. Functional outcome was quantified at first assessment and month 12. RESULTS: PNS prevalence: PNS_1 (27%); PNS_2 (13.2%); PNS_H (13.2%). The prevalence of DS was found to be 3% when applying the PDS. Regardless of the definition being applied, when compared to non-PNS, patients in the PNS group were shown to have significantly worse functioning at month 12. All three PNS definitions showed similar associations with functional outcome at month 12. CONCLUSION: Persistent negative symptoms are present in about 27% of FEP patients with both affective and non-affective psychosis. Although there has previously been doubt as to whether PNS represents a separate subdomain of negative symptoms, the current study suggests that PNS may be more applicable to FEP when compared to DS. Although all three PNS definitions were comparable in predicting functional outcome, we suggest that the PNS definition employed is dependent on the clinical or research objective at hand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".