Demographic and Clinical Factors Associated With Verbal Memory Performance in Patients with Schizophrenia in Hospital Universiti Sains Malaysia (HUSM), Malaysia
Bibliographic record
Abstract
Objective: The present study aims to assess verbal memory performance in patients with schizophrenia attending HUSM and determine the relationship between the patients’ verbal memory performance and their demographic/clinical factors. Methods: A cross sectional study of 114 patients with schizophrenia attending HUSM psychiatric services from December 2007 to May 2008 was conducted. The schizophrenia symptoms as well as verbal memory performance were assessed using the Brief Psychiatric Rating Scale, the Malay version of the Calgary Depression Scale (MVCDS), and the Malay version of the Auditory Verbal Learning Test (MVAVLT). The relationship between verbal memory performance and demographic/clinical symptoms was evaluated using Pearson Correlation. Results: Overall MVAVLT scores in all the trials were lowered in patients with schizophrenia compared to average healthy controls. There were significant relationships between occupational status and MVAVLT performance in Trial A1-A5 Total; between educational level and MVAVLT performance in Trial A1 and Trial A1-A5 Total and between severities of illness; and MVAVLT performance in all indexes except Trial A1 after controlled for occupation and educational level. Conclusions: Patient with schizophrenia in HUSM performed significantly worse than healthy controls in verbal memory with or without interference. There were significant relationships between MVAVLT performance and patient’s occupational status, educational level and severity of the illness but not depressive symptoms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".