Comparing face-to-face and videoconference completion of the Montreal Cognitive Assessment (MoCA) in community-based survivors of stroke
Bibliographic record
Abstract
Introduction Videoconferencing may help address barriers associated with poor access to post-stroke cognitive screening. However, the equivalence of videoconference and face-to-face administrations of appropriate cognitive screening tools needs to be established. We compared face-to-face and videoconference administrations of the Montreal Cognitive Assessment (MoCA) in community-based survivors of stroke. We also evaluated whether participant characteristics (e.g. age) influenced equivalence. Methods We used a randomised crossover design (two-week interval). Participants were recruited through community advertising and use of a stroke-specific database. Both sessions were conducted by the same researcher in the same location. Videoconference sessions were conducted using Zoom. A repeated-measures t-test, intraclass correlation coefficient (ICC), Bland–Altman plot and multivariate regression modelling were used to establish equivalence. Results Forty-eight participants (26 men, M age = 64.6 years, standard deviation ( SD) = 10.1; M time since stroke = 5.2 years, SD = 4.0) completed the MoCA face-to-face and via videoconference on average 15.8 ( SD = 9.7) days apart. Participants did not perform systematically better in a particular condition, and no participant variable predicted difference in MoCA performance. However, the ICC was low (0.615), and the Bland–Altman plot indicated wide limits of agreement, indicating variability between sessions. Discussion Our findings provide preliminary evidence to support the use of videoconference to administer the MoCA following stroke. However, further research into the test–retest reliability of scores derived from the MoCA is needed in this population. Administering the MoCA via videoconference holds potential to ensure that all stroke survivors undergo cognitive screening, in line with recommended clinical practice.
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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 | 0.006 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".