Improving precision in the quantification of cognition using the Montreal Cognitive Assessment and the Mini-Mental State Examination
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) can be used to quantify cognitive ability in older persons undergoing screening for cognitive impairment. Although highly sensitive in detecting mild cognitive impairment, its measurement precision is weakest among persons with milder forms of impairment. We sought to overcome this limitation by integrating information from the Mini-Mental State Examination (MMSE) into the calculation of cognitive ability. METHODS: Data from 185 geriatric outpatients screened for cognitive impairment with the MoCA and the MMSE were Rasch analyzed to evaluate the extent to which the MMSE items improved measurement precision in the upper ability ranges of the population. RESULTS: Adding information from the MMSE resulted in a 13.8% (13.3-14.3%) reduction in measurement error, with significant improvements in all quartiles of patient ability. The addition of three-word repetition and recall, copy pentagons, repeat sentence, and write sentence improved measurement of cognition in the upper levels of ability. CONCLUSIONS: The algorithm presented here maximizes the yield of available clinical data while improving measurement of cognitive ability, which is particularly important for tracking changes over time in patients with milder levels of impairment.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".