Characteristics and duration of untreated illness in correlation with insight level of first time diagnosed schizophrenia patients in rural region of Latvia
Bibliographic record
Abstract
Introduction Improved insight level among schizophrenia patients is predictive for better illness prognosis. Objective Explore factors connected to insight. Aim Evaluate the insight level and clinical characteristics of first time hospitalized schizophrenia spectrum patients. Methods All consecutive first time hospitalized schizophrenia spectrum patients in a psychiatric hospital from 01.01.2016–26.09.2016. Patients were interviewed upon hospitalization and at the discharge with Scale for the assessment of positive symptoms (SAPS) and negative symptoms (SANS), Schedule of assessment of insight-extended (SAI-E), The Calgary depression scale for schizophrenia (C-sch), sociodemographic and clinical data were collected. All participants signed written informed consent and the study was approved by the Riga Stradins University Ethics committee. Results From 45 first episode patients, 38 met the inclusion criteria. Mean age was 37.66 years (SD: 11.48 years), the average duration of untreated illness (DUI) was 40.5 months (SD: 57.35 months). Psychopathologic symptoms and insight levels evaluated in scores in the 1st and 2nd interviews were as follows: SAPS 69.11 (SD: 20.78) and 33.61 (SD: 18.04), SANS 63.21 (SD: 25.30) and 40.95 (SD: 24.47), SAI-E 15.50 and 27.24 (SD: 13.24), P < 0.001, C-sch 8.50 (SD: 5.31) and 4.27 (SD: 2.86), P < 0.05. There was no statistically significant correlation between DUI and insight level. A higher level of insight at hospitalization correlated with higher levels of depression: r = 0.569, P < 0.001. Conclusions We noticed a tendency that lower insight levels might correlate with longer periods of untreated illness. We found that higher insight levels correlated with higher symptoms of depression. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".