Symptoms, cognition, treatment adherence and functional outcome in first-episode psychosis
Bibliographic record
Abstract
BACKGROUND: The differential strength of correlation between symptoms, cognition and other patient characteristics with community functioning in first-episode psychosis has not been fully investigated. METHOD: In a sample of 66 first-episode psychosis patients demographic variables, ratings of pre-morbid adjustment, positive and negative symptoms, duration of untreated psychosis (DUP) and assessment of cognitive functions at baseline, and symptoms, cognitive functions and adherence to medication 1 year, were correlated with scores on social relations and activities of daily living (ADL) (outcome) at 1 year. Hierarchical regression analysis was used to confirm the independent contribution of baseline and concurrent variables to functional outcome at 1 year. RESULTS: Scores on functioning related to social relations and ADL were both significantly correlated with pre-morbid adjustment, all dimensions of residual positive and negative symptoms and adherence to medication at 1 year. Scores on social relations were also modestly correlated with DUP and several cognitive measures at baseline and 1 year (verbal IQ, attention, visual memory, word fluency and working memory). Hierarchical regression confirmed independent contribution of pre-morbid adjustment, total residual symptoms and adherence to medication at 1 year for both dimensions of outcome, and psychomotor poverty and working memory for social relations. CONCLUSIONS: In addition to pre-morbid adjustment potentially malleable variables such as level of residual (but not acute) symptoms, adherence to medication and cognitive deficits are likely to influence outcome on aspects of community functioning in individuals treated for first episode of 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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".