Special Issues on Using the <scp>Montreal Cognitive Assessment</scp> for telemedicine Assessment During <scp>COVID</scp> ‐19
Bibliographic record
Abstract
To the Editor The coronavirus disease 2019 (COVID-19) crisis has accelerated the need for cognitive screening adapted to telemedicine. Understandably, clinicians are trying to use tools in hand. As codevelopers of the Montreal Cognitive Assessment (MoCA1), we have received inquiries on whether and how to adapt the test, what norms are available, and how to validly assess older adults with hearing and/or vision loss. There are modified MoCA versions, including one for telephone administration2 and some that omit visual or auditory items with validated cutoff scores.3, 4 The MoCA website issued an e-mail (March 20, 2020) stating that it has been validated for remote testing. To our knowledge, there are no published validated remote testing adaptations with norms for key groups of interest, including those with assessed sensory abilities. Interpreting test results from remote administrations requires full understanding of the examinee's vision and hearing abilities. Age-related hearing, vision, or dual-sensory loss is highly prevalent (80%5). One cannot assume intact sensory abilities, and the sensory modality influences test performance.3, 6 As a minimum, the examiner should ask: The authors report no conflicts of interest. All authors contributed to the concept and preparation of the letter. None.
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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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".