P3‐200: Cognitive Outcomes Following Transcatheter Aortic Valve Implantation (TAVI)
Bibliographic record
Abstract
TAVI is a minimally invasive method of treating severe aortic stenosis in an elderly, multi-morbid patient population with high surgical risk compared to surgical aortic valve replacement. In aging populations, cognitive deficits are predictive of functional decline, increased mortality, and poorer quality of life. We explored the effects of TAVI on cognition by assessing cognitive performance before and 6 months after TAVI. Patients with severe aortic stenosis referred to the Sunnybrook TAVI clinic were enrolled. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), California Verbal Memory Test-II (verbal memory), Brief Visuospatial Memory Test (visuospatial memory), Digit Symbol-Coding test and Trails Making Test A (speed of processing) and Trails Making Test B (executive function). Test scores were normalized according to patients’ age and years of education and a z score was calculated. Paired t-tests were used to compare scores before and 6 months after TAVI. Of 17 TAVI patients to date (age: 82±6, 52.9% female, years of education: 13.0 ± 4.3), 13 had a MoCA<26 and were impaired on at least one cognitive domain (z-score ≤ -1.5). Executive function (50%) was the most prevalent domain for cognitive deficits followed by visuospatial memory impairment (40%), impaired speed of processing (37.5%) and long term verbal memory impairment (35.3%). There were no significant changes in MoCA (t(7)=0.32, p>0.05) or domain specific cognitive performance 6 months post-surgery (all p>0.05, n=10). These results highlight the prevalence of cognitive impairment in patients referred for TAVI and suggest that deficits may remain unchanged post-surgery. While these preliminary results are reassuring, given the impact of even mild levels of cognitive impairment on quality of life, these findings support the need to screen for these risk factors in TAVI patients to inform future management and treatment strategies with the potential to improve patient care in this elderly population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".