Screening for cognitive impairment after stroke: A systematic review of psychometric properties and clinical utility
Bibliographic record
Abstract
OBJECTIVE: To systematically review the psychometric properties and clinical utility of cognitive screening tools post-stroke. DATA SOURCES: EMBASE, CINAHL, MEDLINE, PsychInfo. STUDY SELECTION: Studies testing the accuracy of screening tools for cognitive impairment after stroke. DATA EXTRACTION: Data regarding the participants, selection criteria, criterion/reference measure, cut-off score, sensitivity, specificity and positive and negative predicted values for the selected tools were extracted. Tools with sensitivity ≥ 80% and specificity ≥ 60% were selected. Clinical utility was assessed using a previously validated tool and those scoring <6 were excluded. DATA SYNTHESIS: Twenty-one papers regarding 12 screening tools were selected. Only the Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE) met all psychometric and clinical utility criteria for any levels of cognitive impairment. However, the MMSE is most accurate to screen for dementia (cut-off score 23/24) and should only be used for this purpose. In addition, the following can be used to detect: • Any impairment: Addenbrooke's Cognitive Examination-Revised (ACE-R), Barrow Neurological Institute Screen for Higher Cerebral Functions (BNIS) and Cognistat. • Multiple-domain impairments: ACE-R, Telephone-MoCA or modified Telephone Interview for Cognitive Status (TICS). • Dementia: TICS; Cambridge Cognitive Examination; Rotterdam-Cambridge Cognitive Examination; Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE) and short-IQCODE. The IQCODE and short-IQCODE are useful when the patient is unable to respond and an informant's view is required. CONCLUSION: The MoCA is the most valid and clinically feasible screening tool to identify stroke survivors with a wide range of cognitive impairments who warrant further assessment.
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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 | 0.017 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".