Cognitive testing following transient ischaemic attack: A systematic review of clinical assessment tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it