A normative calculator for MoCA domain scores: proxy for Z-scores of conventional neuropsychological tests
Bibliographic record
Abstract
BACKGROUND: Test items in MoCA (Montreal Cognitive Assessment) can be used to generate 5 domain scores (i.e. Memory, Language, Attention, Executive and Visuospatial) which have been shown to approximate well-established neuropsychological tests. As neuropsychological tests are known to be affected by age, sex, education, and language of administration, this study derived a regression-based Z-score calculator for MoCA Domain Scores (MDS) that adjusts individual performance for these key confounders; with the intention of improving the clinical utility of MDS as a proxy for conventional neuropsychological tests. METHODS: Participants ≥ 50 years were recruited from Alzheimer's Disease Centers across USA (n = 25,330), and completed MoCA and conventional neuropsychological tests. A subset with normal cognition and global Clinical Dementia Rating of 0 (n = 11,371) was used to derive the Z-score calculator for MDS; while the full sample (n = 25,330) verified the performance of MDS Z-scores in detecting domain-specific impairments (as defined by conventional neuropsychological tests), using areas under the receiver operating characteristic curve (AUC). RESULTS: MDS varied significantly by age, sex, education, and language of administration even among participants with normal cognition. Based on age-, sex-, education-, and language-adjusted Z-scores, the respective AUCs were 91.2% for MoCA-Memory (95%CI 90.7-91.6), 83.6% for MoCA-Language (95%CI 83.0-84.3), 88.7% for MoCA-Attention (95%CI 88.0-89.4), 85.5% for MoCA-Executive (95%CI 84.8-86.1), and 81.0% for MoCA-Visuospatial (95%CI 80.2-81.8). At the commonly-used cut-off of Z-scores ≤ -1.50, all the MDS had specificities of ≥ 80%. CONCLUSIONS: MDS Z-scores can be easily computed using the newly-developed Excel-based calculator, and provide a viable alternative when conventional neuropsychological tests are needed but cannot be feasibly administered, such as in non-specialty clinics with large volume of patients at high-risk of cognitive impairment (e.g. primary-care, geriatric, and stroke-prevention clinics), and, with further validation and calibration, plausibly also in other resource-limited healthcare settings (e.g. in lower- and middle-income countries). They can complement neuropsychological tests as part of the systematic evaluation of cognitive impairment, and help reserve neuropsychological tests for patients most likely to benefit from further evaluation.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".