Utility of the Montreal Cognitive Assessment and Mini-Mental State Examination in Predicting General Intellectual Abilities
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it