Understanding the impact of persistent symptoms in schizophrenia: Cross-sectional findings from the Pattern study
Bibliographic record
Abstract
BACKGROUND: The high societal burden of schizophrenia is largely caused by the persistence of symptoms and accompanying functional impairment. To date, no studies have specifically assessed the course of persistent symptoms or the individual contributions of positive and negative symptoms to patient functioning. The cross-sectional analysis of the Pattern study provides an international perspective of the burden of schizophrenia. METHODS: Clinically stable outpatients from 140 study centers across eight countries (Argentina, Brazil, Canada, France, Germany, Italy, Spain and the United Kingdom) were assessed using clinical rating scales: Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression-Schizophrenia (CGI-SCH) Scale and the Personal and Social Performance (PSP) Scale. Additional measures included patient-reported outcomes, patient socio-demographic variables, living situation, employment and resource use. RESULTS: Overall, 1379 patients were assessed and analyzed and had similar sociodemographic characteristics across countries, with 61.6% having persistent positive and/or negative symptoms. Positive and negative symptoms had been persistent for a mean of 9.6 and 8.9 years (SD: 8.8 and 9.6), respectively. Approximately 86% of patients had a functional disability classified as greater than mild. Patients with a higher PANSS Negative Symptom Factor Score were more likely to have a poorer level of functioning. CONCLUSIONS: This analysis examines individual contributions of persistent positive and negative symptoms on patient functioning in different countries. A high prevalence of patients with persistent symptoms and functional impairment was a consistent finding across countries. Longitudinal observations are necessary to assess how to improve persistent symptoms of schizophrenia and overall patient functioning.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".