Abstract WP559: The NIH Toolbox Cognition Battery Outperforms the MoCA in Detecting Cognitive Impairment Following Mild Stroke in Young Patients
Bibliographic record
Abstract
Introduction: Current Canadian and US guidelines recommend screening for post-stroke cognitive impairment using the Montreal Cognitive Assessment (MoCA), and this metric is often used to assess cognition in stroke trials. The MoCA, however, may lack sensitivity to detect cognitive impairment in young, high-functioning patients with subtle cognitive deficits. We compared differences in performance between the NIH Toolbox-Cognition Battery (NIHTB-CB, an iPad-based 30-minute test that normalizes scores for age, sex, education and ethnicity), and the MoCA in young (18-55 years old), high-functioning (mRS 0-1) people with stroke and age-matched healthy controls. Methods: Recruitment for a target sample size of 120 is ongoing. To date, 21 healthy controls and 21 post-stroke participants are enrolled. Group differences in MoCA, NIHTB-CB fluid cognition, NIHTB-CB crystalized cognition and NIHTB-CB total cognition composite scores were compared using independent t-tests. Effect size of mean differences were calculated using Cohen’s d. The health utility of each group was measured with the EQ-5D. Results: Only the NIHTB-CB fluid cognition and total cognition scores were significantly worse for stroke patients compared to healthy controls, although there was a trend towards lower scores for the NIHTB-CB crystalized cognition and MoCA in the post-stroke group (Table). Stroke patients were 5.3 ± 5.6 months post-stroke. EQ-5D scores were lower for the post-stroke group, indicating worse health status. Conclusions: Our preliminary findings suggest that the NIHTB-CB may be a time-efficient alternative to the MoCA. The NIHTB-CB fluid and total cognition composite scores appear better suited than the MoCA for investigating cognitive impairments in young, mildly disabled stroke 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".