Cognitive Assessments in Randomized Controlled Trials of Acute or Secondary Prevention Stroke Treatments 2011 to 2024: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment after stroke is common and associated with poorer outcomes. However, cognition is rarely assessed in acute or secondary prevention randomized controlled trials (RCTs), and those that do have not been systematically synthesized. This review examines how often cognitive end points are used, specific assessments applied, domains tested, and rates of missing cognitive data. METHODS: We performed a search on EMBASE, PsycINFO, MEDLINE and Cumulative Index to Nursing and Allied Health for RCTs involving adult (≥18 years) participants with stroke, published from 2011 to 2024. Abstracts and full-text publications were screened for stroke RCTs with cognitive end points. These were categorized into acute, secondary prevention, and rehabilitation trials. Descriptive statistics summarized the frequency of cognitive end points, domains, and missing data rates in acute and prevention trials. RESULTS: Of 12 822 screened studies, 980 met criteria for full-text screening and 406 were stroke RCTs with a cognitive end point. Among these, 43 were acute and secondary prevention RCTs eligible for data extraction. The Mini-Mental State Examination (22/43 studies, 52%) and Montreal Cognitive Assessment (18/43 studies, 42%) were used in the most RCTs. There were 66 distinct cognitive tasks used, with greatest diversity in memory tasks (19), executive tasks (11) and global screens (11). Mean missing data were 22.2% (SD: 10.4%, range 0%-62%). CONCLUSIONS: Cognitive tasks are infrequent outcomes in stroke RCTs. When used, the tasks and domains assessed vary widely and are heavily affected by missing data. More pragmatic approaches to measuring meaningful cognitive change in all clinical trial participants are needed.
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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 | 0.062 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".