Low Accuracy of Brief Cognitive Tests in Tracking Longitudinal Cognitive Decline in an Asian Elderly Cohort
Bibliographic record
Abstract
BACKGROUND: Researchers have questioned the utility of brief cognitive tests such as the Mini-Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in serial administration and suggested that brief cognitive tests may not accurately track changes in Global Cognition. OBJECTIVE: To examine the accuracy of longitudinal changes on brief cognitive tests in reflecting progression in Global Cognition measured using comprehensive neuropsychological assessments. METHODS: Two hundred and seven participants were assessed with the MMSE, MoCA, and a validated comprehensive neuropsychological battery. Global z-scores on the battery were derived and used to assess overall and significant (≥0.5 standard deviation) decline on Global Cognition. Different patterns of decline on MMSE/MoCA were classified. Accuracy was examined using receiver operating characteristic curve, and sensitivity, specificity, positive (PPV) and negative (NPV) predictive values were reported. RESULTS: The overall ability of MMSE/MoCA change scores to discriminate participants who did and did not decline on Global Cognition was fair-to-moderate (AUC [95% CI] = 0.71 [0.64-0.78] & 0.73 [0.66-0.80] for overall decline; 0.78 [0.70-0.85] & 0.80 [0.73-0.86] for significant decline, respectively). Changes in MMSE/MoCA had low accuracy in identifying significant Global Cognitive Decline (PPV = 0.41 & 0.46, respectively) but high accuracy in ruling out significant decline and identifying cognitively stable participants (NPV = 0.89 & 0.88, respectively). CONCLUSION: There is limited utility in brief cognitive tests for tracking cognitive decline. Instead, they should be used for identifying participants who remain cognitively stable on follow up. These results accentuate the importance of acknowledging the limitations of brief cognitive tests when assessing cognitive change.
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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 | 0.009 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".