Does Demographic Normalisation Improve the Accuracy of the MoCA and the MMSE for Detecting Mild Cognitive Impairment and Early Dementia ?
Bibliographic record
Abstract
BACKGROUND: The diagnosis of Mild Cognitive Impairment (MCI) is challenging and relies on accurate cognitive testing. To improve the validity of cognitive test results, demographic normalisation is commonly used to adjust test scores for the effects of factors like age, sex and education. Many studies have developed normative tables/calculators based on these demographic factors for cognitive tests widely used to identify MCI - the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). Although demographic normalisation aims to improve diagnostic accuracy, its effectiveness has been seldom studied. This is concerning because, while normalisation improves accuracy for variables unrelated to disease, the commonly adjusted demographic factors-age, sex, and education-are themselves risk factors for MCI and Alzheimer's disease. METHOD: We hypothesized that demographic normalisation reduces diagnostic accuracy when the demographic variables used (age, sex, education) differ systematically between diagnostic groups due to their association with disease risk. We tested this hypothesis by assessing whether normalisation affects the ability of MoCA and MMSE to distinguish MCI and early dementia from cognitively normal (CN) individuals, using receiver operating characteristic analysis with paired bootstrapping across multiple normalisation methods (bin-based, linear, quadratic and partial). RESULT: Surprisingly, demographic normalisation worsened rather than improved diagnostic accuracy for detecting MCI with both MoCA and MMSE, reducing their effectiveness as diagnostic tools and confirming our hypothesis. Demographic normalisation significantly reduced diagnostic accuracy, with AUC decrements of 0.007-0.015 (2-tailed p <0.001) in 7 of 8 CN vs MCI conditions, highlighting that disease-related demographic shifts often negate normalisation benefits and lower the diagnostic performance of MoCA and MMSE. Normalisation failed to consistently improve accuracy for detecting early dementia, with significant decreases in 3 of 8 conditions and a mix of significant and non-significant increases in the others, indicating inconsistent effects on test performance. CONCLUSION: The current findings indicate that normalisation with the commonly used demographic variables (age, sex, and education) has little effect on the accuracy of MoCA & MMSE for identifying MCI and early dementia. Even this little effect often results in a significantly decreased test accuracy, suggesting that demographic normalisation does more harm than good.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 |
| 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".