Dynamic Interplay Between Insight and Persistent Negative Symptoms in First Episode of Psychosis: A Longitudinal Study
Bibliographic record
Abstract
Persistent negative symptoms (PNS) are an important factor of first episode of psychosis (FEP) that present early on in the course of illness and have a major impact on long-term functional outcome. Lack of clinical insight is consistently associated with negative symptoms during the course of schizophrenia, yet only a few studies have explored its evolution in FEP. We sought to explore clinical insight change over a 24-month time period in relation to PNS in a large sample of FEP patients. Clinical insight was assessed in 515 FEP patients using the Scale to assess Unawareness of Mental Disorder. Data on awareness of illness, belief in response to medication, and belief in need for medication were analyzed. Patients were divided into 3 groups based on the presence of negative symptoms: idiopathic (PNS; n = 135), secondary (sPNS; n = 98), or absence (non-PNS; n = 282). Secondary PNS were those with PNS but also had clinically relevant levels of positive, depressive, or extrapyramidal symptoms. Our results revealed that insight improved during the first 2 months for all groups. Patients with PNS and sPNS displayed poorer insight across the 24-month period compared to the non-PNS group, but these 2 groups did not significantly differ. This large longitudinal study supported the strong relationship known to exist between poor insight and negative symptoms early in the course of the disorder and probes into potential factors that transcend the distinction between idiopathic and secondary negative symptoms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".