Schizophrenia - Insight, Depression: A correlation study
Bibliographic record
Abstract
Background: Schizophrenia is one of the severe forms of mental illness which demands enormous personal and economical costs. Recent years have attracted considerable interest in the dual problem of depression in schizophrenia and its relation to insight. Most clinicians believe that poor insight in patients with schizophrenia, though problematic for treatment adherence, may be protective with respect to suicide. Aims and objectives: Our study was aimed to find out the correlation between insight and depression in schizophrenic population. Materials and Methods: Present study was a cross sectional, single centred, correlation study done in total of 60 subjects. Subjects aged between 20 to 60years, diagnosed to have schizophrenia as per ICD-10 and who have given written consent been considered for the study. Subjects who had other psychiatric disorders such as mood disorder, schizoaffective disorder, mental retardation, epilepsy or detectable organic disease and co morbid substance abuse were excluded from the study. Schizophrenics with acute exacerbation were also excluded from the study. For insight assessment, schedule for assessment of insight (SAI), a three item rating scale was used. For the assessment of depressive symptoms, a nine item rating scale, Calgary Depression Rating Scale (CDRS) was administrated. Results: Insight and depression were strongly correlated in schizophrenic population with a Pearson correlation coefficient of 0.758. The correlation between insight and depression was high in subjects with less duration of illness. Conclusions: Our study suggests that poor insight may protect against depression in the early stages of recovery from schizophrenia. The correlation between insight and depression was high in subjects with less duration of illness.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".