VasCog Screen test: sensitive in detecting cognitive impairment in patients who had a stroke or with heart failure
Bibliographic record
Abstract
INTRODUCTION: Vascular diseases, such as stroke and heart failure (HF), are associated with cognitive decline. Vascular cognitive impairment (CI) is commonly found in patients who had a stroke and with HF, ranging from mild CI to dementia. Early detection of CI is crucial for effective management and rehabilitation. This study aimed to develop the VasCog Screen test, a screening tool to detect CI in patients who had a stroke and with HF. METHOD: 427 patients who had a stroke and with HF were assessed using cognitive measures including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and a formal neuropsychological battery. The short-MoCA was derived and combined with Symbol Digit Modalities Test (SDMT) to create the VasCog Screen. The discriminatory ability of different tests for CI was compared, establishing optimal cut-off points. Variants of short-MoCA including the SDMT were also evaluated. RESULTS: Similar prevalence rates of CI were found in stroke and HF cohorts. The most prevalent neuropsychological impairment was visuomotor speed, followed by visual memory and visuoconstruction. More than half of the patients were found to have CI. The VasCog Screen outperformed MMSE, MoCA and short-MoCA in detecting CI. The addition of SDMT to variants of the short-MoCA increased diagnostic accuracy. CONCLUSION: The VasCog Screen test offers a cognitive screening tool, which is sensitive to cognitive deficits characteristically found in patients who had a stroke and with HF. It was found to have good sensitivity, specificity and classification accuracy. It is easy to administer in busy clinics, enabling early detection of CI and facilitating appropriate interventions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".