<p>Montreal Cognitive Assessment — Single Cutoff Achieves Screening Purpose</p>
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The study evaluated the performance between norm-derived age and education adjusted vs single cutoff scores of the Montreal Cognitive Assessment, Hong Kong version (HK-MoCA) in classifying cognitive impairment in Chinese older adults. METHODS: Total scores of HK-MoCA were collected from 315 subjects (128 with dementia, 122 with mild cognitive impairment (MCI) and 65 normal) attending a public district hospital-based cognition clinic from 2012 to 2017. The HK-MoCA total scores were evaluated using different cutoffs. Norm-derived age and education adjusted cutoff scores were at 16th, 7th, and 2nd percentiles. Comparison was made with the single cutoff scores validated in a local study with 21/22 for MCI and 18/19 for dementia. RESULTS: Single cutoff score of HK-MoCA differentiated MCI from normal with sensitivity of 0.861 and specificity of 0.723. To detect dementia, its sensitivity was 0.922, and specificity was 0.923. In identifying cognitive impairment, the sensitivity and specificity were 0.932 and 0.723, respectively. However, age and education adjusted cutoff scores achieved high specificities at all levels of cognitive impairment with trade-off of sensitivities. The accuracy of correctly classifying tested subjects into appropriate groups was 85.3% if single cutoff was used though the consistency between norm-derived cutoffs and expert diagnoses were only 59.0%, 54.2%, and 53.9% at 16th, 7th, and 2nd percentiles, respectively. The consistency decreased with older age and lower education level, and majority of misclassifications were false negatives. CONCLUSION: HK-MoCA is a convenient screening tool to detect cognitive impairment. Administration time is relatively short, and it has incorporated essential cognitive domains. Single cutoff scores with inherent simple education adjustment achieved screening purpose of mild cognitive impairment and dementia in Chinese older adults.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".