Key Words: Montreal Cognitive Assessment, mild cognitive impairment, Alzheimer’s disease, Mini-Mental State Examination
Bibliographic record
Abstract
memory clinic. Method: We administered the MoCA and Mini-Mental State Examination (MMSE) to 32 subjects fulfilling diagnostic criteria for dementia, to 23 subjects fulfilling diagnostic criteria for mild cognitive impairment (MCI), and to 12 memory clinic comparison subjects, at baseline and then at 6-month follow-up. Clinical diagnoses for dementia and MCI were made according to ICD-10 and Petersen criteria. The sensitivity and specificity of both measures were assessed for detection of MCI and dementia. Results: With a cut-off score of 26, the MMSE had a sensitivity of 17 % to detect subjects with MCI, whereas the MoCA detected 83%. The MMSE had a sensitivity of 25 % to detect subjects with dementia, whereas the MoCA detected 94%. Specificity for the MMSE was 100%, and specificity for the MoCA was 50%. Of subjects with MCI, 35 % developed dementia within 6 months, and all scored less than 26 points on the MoCA at baseline. Conclusions: The MoCA is a useful brief screening tool for the detection of mild dementia or MCI in subjects scoring over 25 points on the MMSE. In patients already diagnosed with MCI, the MoCA helps identify those at risk of developing dementia at 6-month follow-up. (Can J Psychiatry 2007;52:329–332) Information on funding and support and author affiliations appears at the end of the article.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".