Cognitive Assessment in Proximity to Acute Ischemic Stroke/Transient Ischemic Attack: Comparison of the Montreal Cognitive Assessment Test and MindStreams Computerized Cognitive Assessment Battery
Bibliographic record
Abstract
BACKGROUND/AIMS: Even mild stroke survivors may sometimes experience residual cognitive damage. No consensus has emerged about which cognitive test is most appropriate for the diagnosis of poststroke cognitive impairment. We aim to compare a computerized battery of neuropsychological tests for memory, attention and executive functions (MindStreams®) with the Montreal Cognitive Assessment (MoCA) to detect mild-to-moderate cognitive impairments in poststroke patients. METHODS: Subjects enrolled to the TABASCO (Tel Aviv Brain Acute Stroke Cohort) study, a prospective study which includes consecutive first-ever mild-to-moderate stroke patients, were included. All participants underwent neurological and cognitive evaluations. RESULTS: A total of 454 patients with transient ischemic attack (TIA) or stroke are reported. Their mean MoCA and MindStreams scores were lower than normal; however, the TIA group presented significantly better scores using either method. The correlation between the MoCA and the computerized global score was 0.6 (p < 0.001). A significant correlation was found between the subcategory scores (executive function, memory and attention). However, the MoCA identified many more subjects with low scores (<26) compared to the MindStreams (70.6 vs. 15.7%). CONCLUSION: Our results demonstrate that either of the modalities alone is sensitive enough for identifying subtle cognitive impairment and none picks up substantially more cognitive losses than the other in patients with cerebrovascular disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".