Cognitive Outcomes following Transcatheter Aortic Valve Implantation: A Systematic Review
Bibliographic record
Abstract
Severe aortic stenosis is the most common valvular heart disease in the elderly in the Western world and contributes to a large proportion of all deaths over the age of 70. Severe aortic stenosis is conventionally treated with surgical aortic valve replacement; however, the less invasive transcatheter aortic valve implantation (TAVI) is suggested for those at high surgical risk. While TAVI has been associated with improved survival and favourable outcomes, there is a higher incidence of cerebral microembolisms in TAVI patients. This finding is of concern given mechanistic links with cognitive decline, a symptom highly prevalent in those with cardiovascular disease. This paper reviews the literature assessing the possible link between TAVI and cognitive changes. Studies to date have shown that global cognition improves or remains unchanged over 3 months following TAVI while individual cognitive domains remain preserved over time. However, the association between TAVI and cognition remains unclear due to methodological limitations. Furthermore, while these studies have largely focused on memory, cognitive impairment in this population may be predominantly of vascular origin. Therefore, cognitive assessment focusing on domains important in vascular cognitive impairment, such as executive dysfunction, may be more helpful in elucidating the association between TAVI and cognition in the long term.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".