StrokeCog-15 Is an Efficient Neuropsychological Battery to Screen for Cognitive Impairment in Chronic Stroke
Bibliographic record
Abstract
BACKGROUND: Poststroke cognitive impairment can significantly impact functional outcomes and quality of life. While comprehensive neuropsychological evaluations are valuable in characterizing this impairment, their time-intensive nature is not always feasible. Thus, we set out to develop a brief cognitive battery that is sensitive to poststroke cognitive impairment. METHODS: Neuropsychological testing was completed in a validation sample of 126 participants with chronic ischemic stroke (median days since stroke, 337 [interquartile range, 235-1057]) as part of StrokeCog, a prospective observational cohort study. This comprehensive 60-minute cognitive battery contained 9 tests covering 5 cognitive domains. A partial least square regression analysis informed the selection of a brief, 15-minute battery of 4 tests (StrokeCog-15) covering 4 cognitive domains: language, memory, working memory, and processing speed/executive functioning. We then compared StrokeCog-15 with Montreal Cognitive Assessment and an established 30-minute battery in its ability to detect cognitive impairment as identified by the comprehensive battery. Finally, we assessed the utility of StrokeCog-15 in an external validation sample of 61 participants (median days since stroke, 210 [interquartile range, 193-230]) enrolled in the parallel Stroke-IMPaCT study (Stroke-Immune Mediated Pathways and Cognitive Trajectory). RESULTS: Cognitive impairment was common, occurring in 50% (n=61) and 66% (n=40) of the 2 cohorts. Deficits occurred most frequently in the memory and processing speed/executive functioning domains. In the derivation sample, StrokeCog-15 demonstrated high sensitivity (0.97) and adequate specificity (0.78) in detecting cognitive impairment on the comprehensive battery, outperforming both Montreal Cognitive Assessment (sensitivity, 0.77; specificity, 0.73) and the 30-minute battery (sensitivity, 0.97; specificity, 0.35). StrokeCog-15 similarly demonstrated high sensitivity (0.93) and adequate specificity (0.67) in the validation sample. CONCLUSIONS: A brief 15-minute battery of tests has high sensitivity to detect cognitive impairment as identified on a longer neuropsychological test battery. StrokeCog-15 assesses multiple cognitive domains commonly impacted by stroke and represents an efficient yet effective means to identify chronic poststroke 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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".