Montreal Cognitive Assessment in Mild Cognitive Impairment: Relationship with Cerebrospinal Fluid Biomarkers and Conversion to Dementia
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) is considered a prodromal state of dementia. Abnormal values of cerebrospinal fluid Alzheimer's disease biomarkers (CSF-AD-b) have been associated with a higher risk of conversion to dementia (due to Alzheimer's disease), but studies evaluating the ability of Montreal Cognitive Assessment (MoCA) in this task are lacking. OBJECTIVE: This study aims to investigate the relationship between MoCA and CSF-AD-b, as well as the ability of those tools to predict conversion to dementia. METHODS: Taking advantage of our MCI cohort with biological characterization on longitudinal follow-up (180 patients followed for 62.6 months during which 41.3% converted), we computed MoCA and MMSE z-scores, using Portuguese normative data. The performance in MoCA z-score was correlated with CSF-AD-b and the relative time to conversion and risk according to baseline characteristics were analyzed using Kaplan-Meier analysis and Cox regression models. RESULTS: MoCA z-scores were correlated with Aβ42 (p = 0.026), t-tau (p = 0.033), and p-tau (p = 0.01). Impaired MMSE (p < 0.001) and MoCA z-scores (p = 0.019), decreased Aβ42 (p < 0.001) and increased t-tau (p < 0.001) and p-tau (p < 0.001) were associated with shorter estimated time of conversion. Aβ42 (p < 0.001) and MMSE z-scores (p = 0.029) were independent predictors of conversion. For those with at least 9 years of education, MoCA z-score (p = 0.004) (but not MMSE) was an independent predictor of conversion as well as Aβ42. CONCLUSIONS: This study confirms the role of CSF-AD-b, namely Aβ42, in predicting conversion from MCI to dementia and suggests the utility of MoCA in predicting conversion in highly educated subjects, supporting its use in the evaluation of MCI patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".