Abstract WMP41: Evaluation of a Digital Cognitive Self-Assessment Method for Post-Stroke Cognitive Decline
Bibliographic record
Abstract
Introduction: Post-stroke cognitive decline (PSCD) is a common complication of strokes, and early assessment is crucial. However, outpatient cognitive assessment protocols are inconsistent, leading to missed diagnoses of PSCD. A potential solution is the XpressO application, introduced in 2023 by the creators of the Montreal Cognitive Assessment (MoCA). Because XpressO is self-paced, it can be completed by patients while waiting for an appointment and hence can assess cognition without impacting clinic workflow. Hypothesis: This study aims to investigate the feasibility of using the XpressO online self-administered cognitive assessment and compare its ability to detect PSCD with the MoCA short form (MoCA-sf) at our out-patient stroke clinic. Methods: Patients at the clinic with a history of ischemic or hemorrhagic strokes were included. We used <12 as the cutoff to determine low performance on the MOCA-sf. After their clinic visit, participants consented and completed a screening survey on stroke risk factors, followed by both the MoCA-sf and XpressO assessments. Completion times for both assessments were recorded, and results were analyzed using R Studio. Results: We enrolled 32 patients and 32 completed the assessment. The results of this study found a moderate correlation between Xpresso and MOCA-sf (ρ = 0.645, p = 6.70e-05). When stratified by cognitive function, no significant correlation was observed between XpressO and higher MoCA-sf (≥12) scores (ρ = 0.128, p = 0.691). The average time to complete the assessment was 6.63 minutes for MOCA –sf and 6.28 minutes for Xpresso. This difference was not significant. However, when stratified by cognitive function, there was a significant difference in completion time between XpressO (4.43 minutes) and MoCA-sf (5.83 minutes). Conclusion: The results suggest that XpressO is better at detecting severe cognitive decline but is less sensitive to milder impairments. Time to complete each test differed significantly only in patients with higher cognitive function, with XpressO being faster than MoCA in this group. XpressO’s lack of significant correlation with higher MoCA-sf scores makes it less suitable as a standalone tool for PSCD screening in outpatient stroke clinics. XpressO can be used to identify patients at risk for severe PSCD, but higher XpressO scores should not eliminate the possibility of PSCD in 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".