T123. PERSISTENT NEGATIVE SYMPTOMS IN FIRST EPISODE PSYCHOSIS: PREVALENCE, PREDICTORS AND PROGNOSIS
Bibliographic record
Abstract
Negative symptoms are a core component of schizophrenia. These symptoms have been shown to impact on a range of outcomes, and often are resistant to pharmacological and psychosocial interventions treatment. The goal of this study was to investigate the prevalence, baseline predictors and long-term impact of persistent negative symptoms (PNS) within a large representative cohort of people with first episode psychosis. The study had prospective design. Patients recruited into the OPUS trial (1998–2000) with a first time diagnosis within the schizophrenia spectrum (F20-28) were included. People were classified with persistent negative symptoms, if they experienced enduring negative symptoms that were not secondary to psychotic symptoms, depression or due to medication side effects. Clinical data collected at baseline, 1 year, 2 years and 10 years was used to identify predictors of PNS and long-term outcomes. Full clinical data was available on 369 people. A total of 90 people (24%) displayed PNS, two years after diagnosis. Significant univariable predictors of PNS at baseline were low functioning, male sex, cannabis use, poor pre-morbid social functioning and high levels of negative symptoms. People that displayed PNS had significantly lower functioning and higher levels of psychopathology at 10 year follow-up. A total 3% of people with PNS were recovered at 10 year follow-up compared to rate of 20% recovered without PNS (OR 7.42, p<0.01). A significant proportion of the cohort displayed persistent negative symptoms and these symptoms significantly impacted on long-term outcomes. Researchers and clinicians need to continue to develop effective interventions that can ameliorate these symptoms and potentially impact on illness prognosis within schizophrenia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".