A 12‐month outcome study of insight and symptom change in first‐episode psychosis
Bibliographic record
Abstract
AIM: We first aimed to evaluate the progression of insight and psychopathology over the first year of treatment for a psychosis. We hypothesized that improvement in insight would associate with improvement in positive and negative symptoms, and depressive and anxious symptom exacerbation. Secondly, in an exploratory analysis, we aimed to identify quantitatively distinct insight trajectory groups, and to describe the impact of psychopathology over time on the different trajectory groups. METHODS: One-hundred and sixty-five patients were administered a comprehensive clinical evaluation, and insight was rated on the Scale for Assessment for Unawareness of Mental Disorder, item 1 (awareness of mental disorder), at admission and after 1, 2, 3, 6, 9 and 12 months. RESULTS: In a generalized estimating equation (GEE) model of change, insight improved concurrently with positive, negative and anxious symptoms between baseline and month 1 in the entire cohort. Latent group-based trajectory analysis revealed five insight groups: good, increasing, decreasing, moderate poor and very poor. GEE modelling revealed that the very poor and moderate poor insight groups displayed greater overall negative symptoms than patients with good and increasing insight trajectories. The good insight group showed significantly greater overall depressive symptoms than the diminished and very poor insight groups. CONCLUSIONS: The results suggest that specific longitudinal insight trajectories were driving the observed associations between insight and negative and depressive symptoms in the entire first-episode psychosis cohort. Persistently poor insight may be an important factor in negative symptom maintenance. Good or increasing course of insight may be early clinical indicators of a liability to depression.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".