Cognitive impairment in cerebral venous congestion: The need for improved assessment tools – a literature review
Bibliographic record
Abstract
ObjectiveCognitive impairment is increasingly recognized in patients with cerebral venous congestion (CVC), yet the cognitive tools used are largely adapted from stroke and dementia research. This review examines current literature on cognitive function in CVC, including conditions such as cerebral venous sinus thrombosis (CVST), idiopathic intracranial hypertension (IIH), and dural arteriovenous fistulas (dAVFs). Special emphasis is placed on the limitations of common screening tools like the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Cognitive dysfunction-primarily affecting executive function, processing speed, and attention-is reported in up to 86% of CVC cases, yet only about 20% of studies use validated cognitive testing tools, most of which may lack sensitivity for CVC-specific deficits. Emerging techniques such as ocular motor testing and physiological markers show promise but require further validation. There is an urgent need for standardized, CVC-specific cognitive assessment protocols that reflect the unique pathophysiology of venous disorders. Future research should prioritize the development of targeted cognitive batteries and integrate objective physiological measures to enhance diagnostic accuracy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".