Utility of combination of Mini-Mental State Examination and Montreal Cognitive Assessment to predict incident Alzheimer's disease dementia in patients at high vascular risks
Bibliographic record
Abstract
BackgroundScreening for cognitive function, using tests such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), is the first step in detecting mild cognitive impairment (MCI) and dementia. However, the sensitivity of detecting MCI patients who will develop incident Alzheimer's disease (AD) dementia in the near future is low.ObjectiveThis study aimed to clarify the utility of the combination of the MMSE and MoCA in selecting patients at a high risk of incident AD dementia.MethodsIn this post-hoc analysis, we derived data from a Japanese observational registry of patients with vascular risk factors. The primary outcome was incident AD dementia. The accepted cutoff values of an MMSE score of 28 and an MoCA score of 26 for MCI were considered.ResultsAfter excluding those who did not undergo the test, 940 patients were included. During a median follow-up period of 4.6 years, incident AD dementia occurred in 49 patients. Patients diagnosed with MCI with MMSE scores <28 or MoCA scores <26 showed a significantly higher risk of AD dementia than those with normal MMSE or MoCA groups. However, patients who met the MCI criteria in only one test showed a risk similar to that of the normal group. In contrast, patients who met the MCI criteria for MMSE and MoCA scores had a 20.65-fold higher risk than those with normal MMSE and MoCA scores.ConclusionsPatients who met the MCI criteria for both the MMSE and MoCA were highly susceptible to incident AD dementia.Clinical Trial RegistrationUMIN000026671.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".