The correlation Between Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment in Indonesian Version (MOCA-INA) Score as Cognitive Function Evaluation Instrument on Patient With Epilepsy In Mataram
Bibliographic record
Abstract
Background: Epilepsy is the highest prevalence diseases in neurology. The decreased of cognitive function is one of the complications which is important. The purpose of this study is to determine the correlation between Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment in Indonesian Version (MoCA-Ina) score on patient with epilepsy in Mataram. Method: This is an observational cross-sectional study with 56 subjects with epilepsy from West Nusa Tenggara 2 center hospital. Data that collected from the samples were MMSE and MoCA-Ina score. Beside of that patients’ characteristics data were also collected which is age, gender, education, seizure control, etiology of epilepsy, seizure type, antiepileptic drugs treatment, and smoking status from the subjects. Both instrument’s mean scores were analyzed for the correlation using Spearman correlation test. Result: This study showing that the subjects average age were 32.9 years old, female (55.4%), and low educated (62.5%). According to the clinical characteristics most subjects were not-depressed (67%), poor quality-controlled seizure (55.4%), idiopathic etiology (66.9%), generalized seizure (71.4%), and having monotherapy (85.7%). Most of the subjects were not a smoker (76.8%). In this study both the mean score of MMSE and MoCA-Ina were 25.3 and 21.3. MMSE and MoCA-Ina score mean have a strong correlation (r=0.766; p<0.001). Conclusion: MMSE and MoCA-Ina score mean on patients with epilepsy have a strong correlation. Therefore, both instruments could be used to evaluate cognitive function on patients with 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 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".