Cognitive screening instruments to identify vascular cognitive impairment: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Vascular cognitive impairment (VCI) is common and important to detect as controlling risk factors, particularly hypertension, may slow onset and progression. There is no consensus as to which cognitive screening instrument (CSI) is most suitable for VCI. We systematically reviewed the psychometric properties of brief CSIs for vascular mild cognitive impairment (VMCI) and vascular dementia (VaD). METHODS: Literature searches were performed using scholarly databases from inception until 31 May 2018. Studies were eligible if participants were aged 18 or older, interviewed face-to-face, and standard diagnostic criteria for VCI were applied, excluding those specifically identifying post-stroke dementia. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool. RESULTS: Fifteen studies were identified including eight types of CSIs (27 subtests/variants) and 4575 participants (1015 with VCI), mean age range: 51.6 to 75.5 years. Most studies compared more than one instrument. Five papers examined clock-drawing; four, the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE); and three used the Brief Memory and Executive Test (BMET). The MoCA (AUC > 0.90) and MMSE (AUC: 0.86-0.99) had excellent accuracy in differentiating VaD from controls; the MoCA had good internal consistency (Cronbach's α: .83-.88). The MoCA (AUC: 0.87-0.93) and BMET (AUC: 0.94) had the greatest accuracy in separating VMCI from controls. Most studies had low to moderate risk of bias in all domains of the QUIPS. Data were heterogeneous, precluding a meta-analysis. CONCLUSIONS: Although few studies were available and further research is required, data suggests that the MoCA is accurate and reliable for differentiating VaD and VMCI from controls.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".