Abstract TP45: Advancements in Digital Cognitive Assessments for Post-Stroke Patients: A Scoping Review
Bibliographic record
Abstract
Introduction: Standardized cognitive assessments such as the Montreal Cognitive Assessment (MOCA) and Mini-Mental State Examination (MMSE) are generally administered using paper-and-pencil methods. Technological advancements have digitized these exams and expanded cognitive testing capabilities in the post-stroke population. Methods: Studies from 2010-2022 were identified from PubMed, Embase, Web of Science, Cumulated Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, and Google Scholar to include digital cognitive assessments utilized for acute and chronic ischemic and hemorrhagic stroke patients. The research questions aim to evaluate technical aspects of digital tests, digital tool effectiveness, cognitive domains assessed, study population characteristics, patient usability, and exam feasibility. The methodological framework for this review included research question identification, relevant study collection, final study selection, data extraction, analysis, and summary. Covidence was used to compile relevant studies. Results: 72 articles were included for final analysis. 8 different digital methods (e.g., tablet, computer, virtual reality) were used to assess cognition, with 26 studies creating a new cognitive test and 24 creating a cognitive test based on a standardized exam. Participants were tested in both acute and chronic phases (5 strictly in acute, 55 strictly in subacute/chronic, and 11 in both). 58% of articles assessed ischemic and hemorrhagic stroke participants, and 9 studies only tested aphasia patients. Exams consisted of a variety of cognitive domains, with the majority of studies testing multiple domains (e.g., executive functioning, attention, and visuospatial processing), and some studies testing only one cognitive domain. The average rate of digital test completion was 95%. Validation of the digital tool was compared with a standardized, paper-and-pencil test (e.g., MOCA, MMSE) in 48 articles (67%). An overall positive satisfaction with the digital test was seen in 8 articles that incorporated patient questionnaires. Conclusion: This review suggests that post-stroke digital cognitive assessments are feasible in the acute and post-acute settings across multiple domains similar to the MOCA and MMSE. Enhancements in these tools will expand access to testing and allow for increased identification of post-stroke cognitive impairment.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".