Screening utility of the Montreal Cognitive Assessment (MoCA): in place of – or as well as – the MMSE?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: This aim of this study was to assess the clinical utility of the Montreal Cognitive Assessment (MoCA) as a screening instrument for cognitive impairment in patients referred to a memory clinic, alone and in combination with the Mini-Mental State Examination (MMSE). METHODS: This was a pragmatic prospective study of consecutive referrals attending a memory clinic (n = 150) over an 18-month period. Patients were diagnosed using standard clinical diagnostic criteria for dementia (DSM-IV) and mild cognitive impairment (MCI; cognitive impairment prevalence = 43%) independent of MoCA test scores. RESULTS: MoCA proved acceptable to patients and was quick and easy to use. Using the cut-offs for MoCA and MMSE specified in the index paper (≥26/30), MoCA was more sensitive than MMSE (0.97 vs 0.65) but less specific (0.60 vs 0.89), with better diagnostic accuracy (area under Receiver Operating Characteristic curve 0.91 vs 0.83). Downward adjustment of the MoCA cut-off to ≥20/30 maximized test accuracy and improved specificity (0.95) for some loss of sensitivity (0.63). Combining MoCA with the MMSE - either in series or in parallel - did not improve diagnostic utility above that with either test alone. CONCLUSIONS: In a memory clinic population, MoCA proved sensitive for the diagnosis of cognitive impairment. Use of a cut-off lower than that specified in the index study may be required to improve overall test accuracy and specificity for some loss of sensitivity in populations with a high prior probability of cognitive impairment. Combining the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) with the MMSE did not improve diagnostic utility.
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.
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 | 0.001 | 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) | 0.005 | 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