Comparison between the accuracy of Montreal Cognitive Assessment and Mini-Mental State Examination in the detection of mild cognitive impairment
Bibliographic record
Abstract
Abstract Introduction:Ageing can cause major changes in the central nervous system of the body, resulting in cognitive decline and associated disorders. Therefore, there is a growing need for an effective cognitive screening method to enhance the diagnosis of mild cognitive impairments and to prevent occurring dementia and Alzheimer's Disease (AD). Our study aimed to compare the accuracy of MMSE (Mini-Mental State Examination) and MoCA (Montreal Cognitive Assessment) while evaluating the independent and interaction effects of age and educational level on these screening tools in a healthy sample.Method: The data for the current study was based on the registration phase of the study during 2016-2018 in Neyshabour Longitudinal Study on Ageing (NeLSA). Both the MoCA and MMSE tests were used to assess cognitive decline among 3326 participants aged 50-94 years of old. The ROC curve analysis and the predictive values were performed to evaluate the diagnostic accuracy of MMSE to discriminate Mild Cognitive Impairment (MCI) from the cognitively healthy adult basis of MoCA scores as a gold test. A two-way ANCOVA was run to examine the effect of Age and Education level on MoCA and MMSE score, while controlling for a gender effect. Data were analyzed using MedCalc Statistical Software version 13.0.6 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2014). Results: The chi-square test shows that MoCA ((72% and 90%) significantly (p-value<0.001() classified more persons as cognitively impaired than the MMSE (45.1%), respectively; using a cutoff score of 24 on the MMSE, 23 and 26 on the MoCA. The cut-off point of below 25 yielded the highest Youden J index for the MMSE in discrimination between MCI and healthy basis of MOCA<23 with an AUC of 0.9 (95% CI: 0.89-0.91) and MOCA<26 with an AUC of 0.87 (95% CI: 0.86-0.89). A two-way ANCOVA results show that the effect of education variable on the MMSE and MoCA score is more important than the age variable.Discussion: Although the cut-off scores give a clear indication of the sensitivity and specificity, they are unable to monitor the impact of confounders, which increase the risk of incorrect classification. Taken together, these findings demonstrate the use of demographically adjusted MoCA and MMSE scores that could provide clinicians with a more reliable estimation of the severity of cognitive impairment, thus increasing the instrument's clinical usefulness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".