Working Memory Impairment in Transient Ischaemic Attack: N-back as a Sensitive Measure for Detection
Bibliographic record
Abstract
BACKGROUND: Transient ischaemic attack (TIA) can lead to lasting changes in brain structure and function resulting in cognitive impairment. Cognitive screening tools may lack sensitivity for detecting cognitive impairments, particularly executive function, which tends to be the earliest affected domain in vascular cognitive impairment. AIM: In this preliminary study, we examine a working memory (WMem) task as a sensitive measure of cognitive impairment in TIA. METHOD: Patients referred to a TIA clinic for transient neurological symptoms completed a general cognitive screening tool (Montreal Cognitive Assessment; MoCA), and a WMem task (2-N-back) in a cross-sectional design. RESULTS: TIA patients (n = 12) showed significantly reduced WMem performance on the N-back compared to patients diagnosed with mimic clinical conditions with overlapping symptoms (n = 16). No group differences were observed on the MoCA. CONCLUSIONS: Assessing WMem may provide a sensitive measure of cognitive impairment after TIA, with implications for cognitive screening in TIA services to triage patients for further neuropsychological support, or for interventions to prevent vascular dementia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".