Cognitive Impairment among Patients with Schizophrenia Attending Psychiatric Services in BPKIHS
Bibliographic record
Abstract
Introduction: This study at the B.P. Koirala Institute of Health Sciences investigated cognitive impairments in schizophrenia, focusing on their prevalence and correlation with psychotic symptoms. Methodology: A cross-sectional observational design was employed, assessing 67 schizophrenia patients diagnosed according to the International Classification of Diseases (ICD-10). The Montreal Cognitive Assessment (MoCA) and the Positive and Negative Syndrome Scale (PANSS) were used to evaluate cognitive functions and symptom severity. Results: The results revealed significant cognitive impairments correlated with the predominance of psychotic symptoms. Notably, patients with predominantly negative symptoms exhibited more severe cognitive deficits. Additionally, the study found that different antipsychotic treatment regimens, particularly the use of second-generation antipsychotics, influenced cognitive outcomes. Conclusion: Cognitive impairments in schizophrenia patients are significantly associated with the type and severity of psychotic symptoms. The effectiveness of antipsychotic regimens in managing these impairments highlights the need for personalized treatment approaches. Recommendation: Future research should focus on exploring tailored antipsychotic strategies and comprehensive cognitive therapies to improve treatment responses and quality of life in schizophrenia patients.
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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 | 0.001 | 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) | 0.002 | 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".