Measurement Error, Reliability, and Minimum Detectable Change in the Mini-Mental State Examination, Montreal Cognitive Assessment, and Color Trails Test among Community Living Middle-Aged and Older Adults
Bibliographic record
Abstract
BACKGROUND: Knowing the reliability of cognitive tests, particularly those commonly used in clinical practice, is important in order to interpret the clinical significance of a change in performance or a low score on a single test. OBJECTIVE: To report the intra-class correlation (ICC), standard error of measurement (SEM) and minimum detectable change (MDC) for the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Color Trails Test (CTT) among community dwelling older adults. METHODS: 130 participants aged 55 and older without severe cognitive impairment underwent two cognitive assessments between two and four months apart. Half the group changed rater between assessments and half changed time of day. RESULTS: Mean (standard deviation) MMSE was 28.1 (2.1) at baseline and 28.4 (2.1) at repeat. Mean (SD) MoCA increased from 24.8 (3.6) to 25.2 (3.6). There was a rater effect on CTT, but not on the MMSE or MoCA. The SEM of the MMSE was 1.0, leading to an MDC (based on a 95% confidence interval) of 3 points. The SEM of the MoCA was 1.5, implying an MDC95 of 4 points. MoCA (ICC = 0.81) was more reliable than MMSE (ICC = 0.75), but all tests examined showed substantial within-patient variation. CONCLUSION: An individual's score would have to change by greater than or equal to 3 points on the MMSE and 4 points on the MoCA for the rater to be confident that the change was not due to measurement error. This has important implications for epidemiologists and clinicians in dementia screening and diagnosis.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".