Comparative assessment of Montreal Cognitive Assessment (MOCA) and Minimental State Examination (MMSE) in apolipoprotein E (APOE) ɛ4 allele carriers in epilepsy
Bibliographic record
Abstract
Abstract Background/Aim Mini mental state examination (MMSE) is a widely accepted tool till date to investigate cognitive status; however, its sensitivity is questioned by few studies. Alternately, Montreal cognitive assessment (MOCA) is considered more effective with high sensitivity to assess cognitive status than MMSE. The usefulness of MOCA is well established in assessing cognitive status in patients in various disorders. Apolipoprotein E (APOE) ɛ4 allele is identified as one of the risk factors associated with cognitive impairment on MMSE; however, the usefulness of MOCA on the association between APOE ɛ4 allele and cognitive impairment is not clearly established and hence the present study. Methods This prospective study recruited 123 subjects diagnosed as tonic-clonic seizures in the study site during the study period. Results Gender and educational status showed normal cognitive function on MMSE but showed cognitive impairment on MOCA. Among epilepsy patients, all APOE ɛ4 carriers showed mild to severe cognitive impairment on MOCA but differences in cognitive status were observed in this population as well as in APOE ɛ4 non-carriers on MMSE. Conclusion Thus, the present study demonstrates the sensitivity of MOCA over MMSE in detecting cognitive impairment in epilepsy.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".