C-36 Montreal Cognitive Assessment for Dementia Severity Rating in a Diagnostically Heterogeneous Clinical Cohort
Bibliographic record
Abstract
Abstract Objective The Mini Mental State Exam (MMSE) has enjoyed widespread use as a dementia severity staging instrument (Perneczky et al., 2006). More recently, the Montreal Cognitive Assessment (MoCA; Nasreddine, 2005) has been advanced as a potentially superior measure with enhanced sensitivity to Mild Cognitive Impairment (MCI). To the authors’ knowledge, there are no published guidelines for staging dementia severity with the MoCA. The aim of this study was to evaluate the utility of the MoCA for dementia severity staging. Method Participants (N = 162) were drawn from a diagnostically heterogeneous retrospective sample of referrals to a multidisciplinary memory clinic. Participants were categorized as MCI, mild dementia, or moderate dementia using the Quick Dementia Rating System (QDRS) sum of boxes score. Receiver operating characteristics of the MoCA were calculated using MATLAB and optimal cutpoints were determined using Youden’s Index. Results The MoCA demonstrated some utility in differentiating MCI from all severity dementia as defined by the QDRS, with an optimal cutpoint of 17 (AUC = .75). Cut points of 17 and 14 best separated MCI from mild dementia (AUC = .72) and mild from moderate dementia (AUC = .66), respectively. These cutpoints were associated with modest sensitivity (.50 - .53) and reasonable specificity (.76 - .87). Average diagnostic accuracy was 69.5%. Conclusions This study suggests that the MoCA has some utility for dementia severity staging. Future work should replicate these findings in other clinical cohorts. The use of the QDRS (an informant report measure) as the severity criterion is a significant limitation of the present study.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".