Short-term Functional Outcome in Psychotic Patients. Results of the Turku Early Psychosis Study (TEPS).
Bibliographic record
Abstract
BACKGROUND: Functional recovery of patients with clinical and subclinical psychosis is associated with clinical, neuropsychological and developmental factors. Less is known about how these factors predict functional outcomes in the same models. We investigated functional outcomes and their predictors in patients with first-episode psychosis (FEP) or a confirmed or nonconfirmed clinical high risk of psychosis (CHR-P vs. CHR-N). METHODS: Altogether, 130 patients with FEP, 60 patients with CHR-P and 47 patients with CHR-N were recruited and extensively examined at baseline (T0) and 9 (T1) and 18 (T2) months later. Global Assessment of Functioning (GAF) at T0, T1 and T2 and psychotic, depression, and anxiety symptoms at T1 and T2 were assessed. Functional outcomes were predicted using multivariate repeated ANOVA. RESULTS: During follow-up, the GAF score improved significantly in patients with FEP and CHR-P but not in patients with CHR-N. A single marital status, low basic education level, poor work situation, disorganization symptoms, perceptual deficits, and poor premorbid adjustment in patients with FEP, disorganization symptoms and poor premorbid adjustment in patients with CHR-P, and a low basic education level, poor work situation and general symptoms in patients with CHR-N predicted poor functional outcomes. Psychotic symptoms at T1 in patients with FEP and psychotic and depression symptoms at T1 and anxiety symptoms at T2 in patients with CHR-P were associated with poor functioning. CONCLUSIONS: In patients with FEP and CHR-P, poor premorbid adjustment and disorganization symptomatology are common predictors of the functional outcome, while a low education level and poor work situation predict worse functional outcomes in patients with FEP and CHR-N. Interventions aimed at improving the ability to work and study are most important in improving the functioning of patients with clinical or subclinical psychosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".