Feasibility of telephone and computerized cognitive testing as a secondary outcome in an acute stroke clinical trial: A mixed methods sub-study of the AcT Trial
Bibliographic record
Abstract
Abstract Introduction Post-stroke cognitive impairment is associated with impaired quality of life. Remote testing provides a potential avenue to measure cognitive outcomes efficiently. Patients and Methods Prospective cognitive outcomes were collected at 90–180 days using both telephone MoCA (T-MoCA; range 0–22; <17 impairment) and Creyos, a computerized cognitive battery. Key variables associated with completion were assessed using logistic regressions. Mixed methods brief structured interviews and exit survey were performed to explore barriers to completing computer testing. Results Of 791 potentially eligible patients (mean age 70 ± 14 years), there was low feasibility of remote cognitive testing, with only 401 (51%) completing the T-MoCA, and 242 (31%) completing Creyos. Our regression models show that age (ORT-MoCA: 0.95 (95% Confidence Interval (CI): 0.94–0.97); ORCreyos: 0.95 (95% CI: 0.94–0.96)), functional impairment (mRS 2–5; ORT-MoCA: 0.55 (95% CI: 0.37–0.81); ORCreyos: 0.66 (95% CI: 0.44–0.98)), quality of life (EQ-VAS; ORT-MoCA: 1.02 (95% CI: 1.01–1.03); ORCreyos: OR:1.02 (95% CI: 1.01–1.03)) and length of hospital stay (ORT-MoCA: 0.98 (95% CI: 0.96–0.99); ORCreyos: 0.97 (95% CI: 0.94–0.99)) predicted both telephone and computer cognitive test completion; computer literacy predicted computer test completion (ORCreyos: 1.12 (95% CI: 1.04–1.21)). In interviews, a preference for accessibility of computerized testing was reported. Discussion Remote cognitive testing has limited feasibility as a secondary outcome in large acute stroke trials. Patients who are older, with worse quality of life, or severe functional impairment post-stroke are less likely to complete remote cognitive outcomes. Conclusion Innovative approaches to post-stroke cognitive outcomes in acute stroke trials are needed. Data Access Data available upon request.
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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 | 0.045 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".