Cognitive testing following transient ischaemic attack: A systematic review of clinical assessment tools
Bibliographic record
Abstract
Cognitive deficits are prevalent after transient ischaemic attack (TIA) and result in loss of function, poorer quality of life and increased risks of dependency and mortality. This systematic review aimed to synthesise the available evidence on cognitive assessment in TIA patients to determine the prevalence of cognitive deficits, and the optimal tests for cognitive assessment. Medline, Embase, PsychINFO and CINAHL databases were searched for relevant articles. Articles were screened by title and abstract. Full-text analysis and quality assessment was performed using the National Institute of Health Tool. Data were extracted on study characteristics, prevalence of TIA deficits, and key study findings. Due to significant heterogeneity, meta-analysis was not possible. Twenty-five full-text articles met the review inclusion criteria. There was significant heterogeneity in terms of cognitive tests used, definitions of cognitive impairment and TIA, time points post-event, and analysis methods. The majority of studies used the Mini-Mental State Examination (MMSE) or Montreal Cognitive Assessment (MoCA) (n = 23). Prevalence of cognitive impairment ranged from 2% to 100%, depending on the time-point and cognitive domain studied. The MoCA was more sensitive than the MMSE for identifying cognitive deficits. Deficits were common in executive function, attention, and language. No studies assessed diagnostic test accuracy against a reference standard diagnosis of cognitive impairment. Recommendations on cognitive testing after TIA are hampered by significant heterogeneity between studies, as well as a lack of diagnostic test accuracy studies. Future research should focus on harmonising tools, definitions, and time-points, and validating tools specifically for the TIA population.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".