Sensitivity of Montreal CognitiveAssessment in Comparison with Mini MentalStatus Examination in Testing CognitiveStatus in Epilepsy Patients with PhenytoinMonotherapy
Bibliographic record
Abstract
Background and objective: Most studies follow Mini Mental Status Examination (MMSE) to assess cognitive status in epilepsy population with phenytoin monotherapy, however its sensitivity on detecting cognition in this population remains debatable. Previous study observed Montreal Cognitive Assessment (MOCA) being more sensitive in testing cognitive status in patients with stroke, parkinsonism, cardiovascular events, epilepsy etc. Therefore, in the present study, we examined the sensitivity of MOCA compared with MMSE in testing the cognitive status in epilepsy population with phenytoin monotherapy. Method: This case-control study enrolled 63 newly diagnosed epilepsy patients (controls) and 60 epilepsy patients with phenytoin monotherapy up to one year. Both controls and cases were screened using MMSE and MOCA for cognitive function. Results: MOCA showed cognitive impairment 70% in controls and 100% in cases as compared to 12% in controls and 94% in cases with MMSE. Our findings indicate that MOCA could detect cognitive impairment that failed with MMSE in both controls and cases, possibly the MOCA subscales contributed to contributed to higher sensitivity of MOCA as compared to MMSE. Conclusion: MOCA was found to be more sensitive and reliable than MMSE in testing the cognitive status in epilepsy population with phenytoin monotherapy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".