Factors Associated with Cognitive Score in People with Schizophrenia at Prof. Dr. M. Ildrem Mental Hospital Medan
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is characterized by being a condition with complex symptomatic dimensions. Its prognosis is poor due to the impairment of multiple cognitive functions, which handicaps the adequate social, academic, or employment reintegration of the patient. Cognitive impairment refers to the loss of cognitive functions, specifically memory, attention, and speed of information processing. A wide range of cognitive functions is affected, particularly memory, attention, motor skills, executive function, and intelligence. METHODS: This study is a multivariate predictive conceptual framework study with a cross-sectional approach to 120 subjects at the Prof. Dr. M Ildrem Mental Hospital Medan in May 2020–July 2020 using a sample that is a consecutive sampling. The test conducted in this study consisted of a bivariate test and a multivariate linear regression test to determine the factors that were associated with the cognitive score. The measuring instrument used is the Montreal Cognitive Assessment (MoCA) Ina. RESULTS: In the bivariate test, in gender variable (p = 0.644) and age variable (p = 0.255) were not statistically significant, so the variables were not included in the multivariate test. In marital status variable (p = 0.0001), type of antipsychotic (p = 0.193), income/month (p = 0.0001), length of education (p = 0.0001), length of illness (p = 0.0001), frequency hospital admission (p = 0.0001), duration of untreated psychosis (DUP) (p = 0.0001), positive and negative syndrome scale (PANSS) scale (p = 0.141), and negative PANSS scale (p = 0.0001) were found statistically significant for the total MoCA Ina score on the bivariate test. After multivariate linear regression testing, the statistically significant variables on the total MoCA Ina score were negative PANSS scale (p = 0.001), income/month (p = 0.0001), length of education (p = 0.001), length of illness (p = 0.0001), DUP (p = 0.028), and marital status (p = 0.0001). CONCLUSION: By knowing the factors related to the total score of MoCA Ina, it is expected that clinicians can be more careful in giving treatment interventions for people with schizophrenia who are at risk for cognitive impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".