Duration of active psychosis and first‐episode psychosis negative symptoms
Bibliographic record
Abstract
AIM: Duration of untreated psychosis (DUP) has been associated with negative symptoms in several studies; however, longitudinal findings have been inconsistent. No previous study has accounted for active psychosis after presentation, although this could impact on outcomes in a manner similar to DUP. METHODS: We measured Scale for the Assessment of Positive Symptoms at frequent intervals during the 12 months after initial presentation to determine the active psychosis duration for 230 individuals with first-episode psychosis. This duration was added to DUP prior to presentation to create a new variable, duration of active psychosis (DAP). Negative symptoms were divided into expressivity and motivation/pleasure domains as measured by Scale for the Assessment of Negative Symptoms (SANS). The relationship of DUP and DAP with negative symptoms at 24-month follow up was determined and confounders controlled for using regression analysis. RESULTS: When DUP and DAP were compared as binary variables with long and short groups, 25.2% of individuals had differing category membership. DAP had a significant uncorrected association with both expressivity domain and motivation/pleasure domains at 24 months; however, relationship with DUP was not significant. DAP remained a significant predictor of 24-month expressivity domain after controlling for potential confounders. CONCLUSIONS: Active psychosis after presentation is substantial, which is a limitation of DUP studies if active psychosis is considered as the key factor within DUP. DAP is a better predictor of negative symptoms than DUP at 2-year follow up, which suggests this concept requires further research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".