Neuropsychology in Multidisciplinary Stroke Care: Clinical Feasibility of the NINDS-CSN Vascular Cognitive Impairment Harmonization Standards
Bibliographic record
Abstract
As a significant number of stroke victims exhibit cognitive impairment, neuropsychological assessments can enhance poststroke management by identifying pertinent cognitive sequelae and providing salient care recommendations. However, due to operational differences between traditional neuropsychology and stroke services, neuropsychological assessments often remain underutilized in stroke care. We developed a novel care model that incorporated neuropsychological testing into a comprehensive stroke program using the modified vascular cognitive impairment (VCI) half-hour assessment protocol proposed by the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network (NINDS-CSN). The test batteries were administered during the patients' acute admissions and then again upon follow-up in the multidisciplinary stroke clinic. Patient and provider satisfaction was then evaluated. Surveys revealed high provider satisfaction with improved clinic efficacy, improved data turnaround time, and with value neuropsychology services added to the comprehensive stroke program. Results from the 18-item industry standard Press-Ganey surveys showed all scores above 4.4/5.0 for patient satisfaction. This clinic garnered high provider and patient satisfaction after the first year. The (modified) NINDS-CSN VCI assessment protocol demonstrated clinical feasibility, suggestive of an efficient method of providing focused neuropsychological services in a clinical setting that otherwise prohibits traditional, comprehensive cognitive assessments.
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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 | 0.087 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".