Comparison of the application of montel cognitive assessment and mini-mental state examination to cognitive function evaluation in the stable patients with schizophrenia
Bibliographic record
Abstract
Objective To investigate the application of Montel Cognitive Assessment(MoCA) and Mini-mental State Examination(MMSE) to cognitive function evaluation for patients during the stable phase of Schizophrenia.Methods Patients in stable phase of Schizophrenia were selected and both MoCA and MMSE were conducted to assess their cognitive function.The results were analyzed with SPSS software.Results The detection rate of MoCA was 83.75%,while MMSE was 31.25%.The sensitivity of MoCA was better than MMSE.Conclusion Compared to MMSE,MoCA covers a broader scope of cognitive function,its application is simpler,sensitivity higher and thus is more suitable for evaluating the impairment of cognitive function due to Schizophrenia.Nevertheless,MoCA scores can be affected by the age,education level,course and mental status of the patients and its clinical practice should be included for fair evaluation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".