Proposal for Efficient Access to Cognitive Screening: using MoCA‐XpressO Pre‐screen, MoCA‐Duo Videoconference Screen, and MoCA‐Report for Post‐screen Interpretation
Bibliographic record
Abstract
Abstract Background The continuously aging population worldwide results in increased prevalence of cognitive decline and growing demand for cognitive screening. This demand is expected to increase with newly approved disease modifying therapies. However, scarce resources for professional cognitive assessments, requires efficient screening tools that are accessible and easily applied. Therefore, a simple process of brief self‐administered cognitive pre‐screening, videoconference‐screening, and an automated report with result interpretation, may be highly efficient and will increase access to cognitive screening. Method We integrated validated novel tools for accessible, home‐based cognitive pre‐screening and screening. It includes development and validation of the Montreal Cognitive Assessment (MoCA)‐XpressO: a brief self‐administered cognitive pre‐screening tool, and cognitive screening via MoCA‐Duo: videoconference version of MoCA. Finally, we developed the MoCA‐Report, an automated summary of MoCA‐Duo with result interpretation according to various cognitive domains and suggestions for further inquiries. Result The MoCA‐XpressO includes assessment of memory tasks, logical tasks, and processing speed. Validation of MoCA‐XpressO showed high accuracy (AUC 0.85) therefore may identify the healthy population with no objective cognitive impairment: the “worried‐well” who will not require further screening. Cognitive screening can then be applied via MoCA‐Duo only to selected patients with identified cognitive impairment. MoCA‐Duo by videoconferencing carries significant benefits: (1) allows home‐based validated assessment; (2) allows matching between patients and certified raters across the globe, with similar country of origin and language, which is highly valuable in cognitive evaluations. Ultimately, post‐screening interpretation is provided automatically by MoCA‐Report which presents interpretation of the scores according to normative data, present the Memory Index Score (MIS), conversion to Mini Mental State Examination and Clinical Dementia Rating scores, optional cognitive manifestations, prediction of conversion to dementia, prediction of amyloid pathology, and potential risk for driving errors. It will also explore potential eligibility for newly approved disease modifying therapies. Conclusion The estimated increase in the prevalence of dementia, and newly approved disease modifying therapies are expected to increase demand for cognitive screening. Integration of self‐administered cognitive pre‐screening by MoCA‐XpressO, and videoconference cognitive screening via MoCA‐Duo including an automated report with result interpretation, propose efficient and comprehensive cognitive screening for high volume of 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 | 0.055 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".