Utility of the Montreal Cognitive Assessment and Mini-Mental State Examination in Predicting General Intellectual Abilities
Bibliographic record
Abstract
OBJECTIVE: To determine whether scores from 2 commonly used cognitive screening tests can help predict general intellectual functioning in older adults. BACKGROUND: Cutoff scores for determining cognitive impairment have been validated for both the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). However, less is known about how the 2 measures relate to general intellectual functioning as measured by the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). METHODS: A sample of 186 older adults referred for neuropsychological assessment completed the MoCA, MMSE, and WAIS-IV. Regression equations determined how accurately the screening measures could predict the WAIS-IV Full Scale Intelligence Quotient (FSIQ). We also determined how predictive the MoCA and MMSE were when combined with 2 premorbid estimates of FSIQ: the Test of Premorbid Functioning (TOPF) (a reading test of phonetically irregular words) and a predicted TOPF score based on demographic variables. RESULTS: MoCA and MMSE both correlated moderately with WAIS-IV FSIQ. Hierarchical regression models containing the MoCA or MMSE combined with TOPF scores accounted for 58% and 49%, respectively, of the variance in obtained FSIQ. Both regression equations accurately estimated FSIQ to within 10 points in >75% of the sample. CONCLUSIONS: Both the MoCA and MMSE provide reasonable estimates of FSIQ. Prediction improves when these measures are combined with other estimates of FSIQ. We provide 4 equations designed to help clinicians interpret these screening measures.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".