Prediction of Probable Alzheimer Disease in Patients With Symptoms Suggestive of Memory Impairment: Value of the Mini-Mental State Examination
Bibliographic record
Abstract
BACKGROUND: The Mini-Mental State Examination (MMSE) is a widely used diagnostic tool for dementia. Its use as a predictive indicator of probable Alzheimer disease (AD) has not been established. OBJECTIVES: To determine the accuracy of the MMSE in predicting emergent AD in a sample of patients who were referred because of symptoms suggestive of memory problems and to determine whether an abbreviated version of the MMSE could be developed that would be as accurate as the full MMSE in predicting emergent AD. DESIGN: Inception cohort of participants with symptoms suggestive of memory impairment by their family physicians were given baseline assessments, including MMSE. After 2 years, the participants' conditions were diagnosed following the standard criterion for AD. Diagnosticians were blind to baseline scores. SETTING AND PARTICIPANTS: One hundred eighty-three community-residing participants were referred by their family physicians to a university teaching hospital research investigation. After baseline screening, 165 participants were included in the study who did not have dementia and had no identifiable cause for memory impairment. After 2 years, 29 participants met criteria for AD, 98 did not develop dementia, 18 developed vascular lesions or non-AD dementia, and 20 did not return. MAIN OUTCOME MEASURE: Diagnostic classification of AD or no evidence of dementia. RESULTS: Logistic regression model was significant. At a cutoff score of 24 or less, sensitivity was 31%; specificity, 96%; with a likelihood ratio of 7.75. A reduced model of 2 subtests was identified with a sensitivity of 41%; specificity, 98%; with a likelihood ratio of 20.70. CONCLUSIONS: Results suggest that the full or abbreviated MMSE is useful in predicting emergent AD in patients with positive test results. However, it is not recommended for use as a screening or diagnostic instrument since a negative test result did not rule out emergent AD. It is recommended as a tool to identify those needing closer monitoring.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".