An Automated Montreal Cognitive Assessment MoCA‐Report for Optimal Clinical Management of Alzheimer’s Disease and Initial Eligibility Assessment for Newly Approved Therapies
Bibliographic record
Abstract
Abstract Background The Montreal Cognitive Assessment (MoCA) is one of the cognitive screening tools most frequently used in primary care. Early detection of Alzheimer’s disease will allow rapid intervention, including initiation of recently approved disease modifying therapies, which carry potential benefits for patients, e.g., maintained cognition and function, along with improved prognosis. However, patients with cognitive impairment detected in primary care are commonly referred to specialty clinics for further evaluation. This causes large volume of referrals and long waiting‐time to cognitive specialists which delay diagnosis and treatment. Therefore, an automated report of cognitive screening with rapid interpretation of the results may allow primary care clinicians to identify the patients with high probability for Alzheimer’s disease and potential eligibility to approved therapies. Method We completed an extensive literature review and gathered data derived from the MoCA that is relevant for clinical management and suitability for approved disease‐modifying therapies. Following this, we developed an automated report for the digital‐MoCA software that will present pertinent information that is applicable for clinical management, staging, conversion to other useful well‐known scales, and initial assessment of eligibility for approved disease modifying therapies. Result Based on published studies, we developed a detailed MoCA‐Report to be automatically generated upon completion of the digital‐MoCA. The MoCA‐Report presents the patients’ score on each subtest as well as the total score in comparison to normative data, the Memory Index Score (MIS), MoCA score conversion to Clinical Dementia Rating Scale, Mini Mental State Examination, and risk for amyloid pathology. The MoCA‐Report also includes relevant information on staging, risk for driving errors, and possibility of conversion to dementia. It is designed to provide primary care clinicians assistance in clinical management, initial identification of eligibility for disease modifying therapies, and selection of referrals to specialty clinics, thus improving patient care and supporting the healthcare systems. Conclusion The MoCA‐Report integrates information and interpretation of cognitive screening in an automated modality, with minimal use of staff and time. Rapid interpretation of the cognitive screening in primary care may support clinical management in a timely manner and promote early detection and intervention for patients with Alzheimer’s disease.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".