Tracking Cognitive Decline in Alzheimer's Disease Using the Mini-Mental State Examination: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To estimate the annual rate of change scores (ARC) on the Mini-Mental State Examination (MMSE) in Alzheimer's disease (AD) and to identify study or population characteristics that may affect the ARC estimation. METHODS: MEDLINE was searched for articles published from January 1981 to November 1997 using the following keywords: AD and longitudinal study or prognosis or cognitive decline. The bibliographies of review articles and relevant papers were searched for additional references. All retrieved articles were screened to meet the following inclusion criteria: (a) original study; (b) addressed cognitive decline or prognosis or course of AD; (c) published in English; (d) study population included AD patients with ascertainable sample size; (e) used either clinical or pathological diagnostic criteria; (f) longitudinal study design; and (g) used the MMSE as one of the outcome measures. Data were systematically abstracted from the included studies, and a random effects regression model was employed to synthesize relevant data across studies and to evaluate the effects of study methodology on ARC estimation and its effect size. RESULTS: Of the 439 studies screened, 43 met all the inclusion criteria. After 6 studies with inadequate or overlapping data were excluded, 37 studies involving 3,492 AD patients followed over an average of 2 years were included in the meta-analysis. The pooled estimate of ARC was 3.3 (95% confidence interval [CI]: 2.9-3.7). The observed variability in ARC across studies could not be explained with the covariates we studied, whereas part of the variability in the effect size of ARC could be explained by the minimum MMSE score at entry and number of assessments. CONCLUSIONS: A pooled average estimate of ARC in AD patients was 3.3 points (95% CI: 2.9-3.7) on the MMSE. Significant heterogeneity of ARC estimates existed across the studies and cannot be explained by the study or population characteristics investigated. Effect size of ARC was related to the initial MMSE score of the study population and the number of assessments.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.060 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".