Evaluation of Cognitive Functions in Schizophrenic Patients with the Montreal Cognitive Assessment Scale and Mini-Mental State Examination
Bibliographic record
Abstract
Schizophrenia is a complex neurodevelopmental disorder. Cognitive deficit is the central feature of the neurodevelopmental disorders. Cognitive impairment is related to social, functional, and clinical symptoms. The aim of this study was to investigate the clinical usability of the Montreal Cognitive Assessment (MoCA) as a screening instrument for cognitive impairment in schizophrenic patients alone, and in correlation with the Mini-Mental State Examination (MMSE). This clinical study included 31 patients diagnosed with schizophrenia. Patients were selected from Psychiatry Clinic. For the assessment of cognitive impairment, we used Montreal Cognitive Assessment Scale (MoCA) and Mini-Mental State Examination (MMSE). Of the total number of patients (n=31), 6/31 (19.4 %) were males and 25/31 (80.6 %) were females; the mean duration of the disorder was 23.5 years (SD=6.69). Seventeen patients (54.8%) of those who were on MMSE scale had a score greater or equal to 24 (normal range) and the MoCA scale had a normal score (>21), while 11 (35.5%) patients reported moderate to severe cognitive impairment. Analysis of the correlation coefficient between the total score of MoCA and the MMSE scale indicates a statistically significant positive correlation with Spearman rho=0.81 and P
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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 | 0.004 |
| 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 | 0.000 | 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".