Cognitive Screening Following Stroke: Are We Following Best Evidence‐based Practice in Australian Clinical Settings?
Bibliographic record
Abstract
ObjectiveCognitive screening tools are now recommended by national governing bodies to detect cognitive impairments following stroke and to prompt referral for further comprehensive assessment and rehabilitation. The primary aim of this review was to critically examine and integrate data across clinical and research domains to better understand Australian cognitive screening practices following stroke.MethodData from national clinical guidelines and audits, psychometric research, and clinical practice investigations were sourced, critically examined, and integrated.ResultsNational Australian audit data suggest over two thirds of stroke units are routinely using screening tools to detect cognitive impairment. However, psychometric research suggests traditional cognitive screening tools, such as the Mini‐Mental State Examination, lack sensitivity to detect stroke‐related cognitive impairment. Furthermore, although more recently developed screeners, such as the Montreal Cognitive Examination, possess improved content validity, further modification, and/or supplemented assessment is required to improve their clinical utility. Of additional concern, even when cognitive impairments are detected during cognitive screening, very few stroke patients are referred for further comprehensive assessment as recommended within clinical practice guidelines.ConclusionsCurrent evidence indicates cognitive screening tools, in their current form, do not perform well in stroke populations due to a variety of factors including poor content validity and lack of sensitivity. It appears that most Australian stroke patients with cognitive impairment are not receiving the assessment and rehabilitation services they require. Recommendations to adapt current screening tools, develop new stroke‐specific screening measures, and consider cognitive assessment protocols other than screening are discussed.
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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 | 0.072 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".