Contribution of Informant and Patient Ratings to the Accuracy of the Mini‐Mental State Examination in Predicting Probable Alzheimer's Disease
Bibliographic record
Abstract
OBJECTIVES: To determine whether the accuracy of the Mini-Mental State Examination (MMSE) in predicting future Alzheimer's disease (AD) could be improved by the addition of patient and informant ratings of cognitive difficulties. DESIGN: An inception cohort of nondemented patients followed longitudinally for 2 years. SETTING: Patients referred to a university teaching hospital research investigation by their family physicians because of concerns about memory impairment. PARTICIPANTS: One hundred sixty-five community-residing patients were included who did not have dementia or any identifiable cause for memory impairment. After 2 years, 29 met criteria for AD, and 95 were not demented. MEASUREMENTS: Baseline assessments included MMSE, an Informant Rating Scale, and a Patient Rating Scale of cognitive difficulties. After 2 years, patients were diagnosed following the reference standard for probable AD. Diagnosticians were blind to baseline scores. RESULTS: Age and education were included in all analyses as covariates. The best logistic regression model included the Informant Rating Scale and the MMSE (sensitivity = 83%, specificity = 79%). An empirically reduced six-item model that included two items each from the MMSE, the Patient Rating Scale, and the Informant Rating Scale produced a significantly better model than the one with the full test scores (sensitivity = 90%, specificity = 94%). CONCLUSION: Results indicate that inclusion of informant ratings with the MMSE significantly improved its accuracy in the prediction of probable AD. Replication in a new prospective cohort of nondemented patients is necessary to confirm these findings.
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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 | 0.022 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".