P407 MULTIDIMENSIONAL ASSESSMENT OF THE ELDERLY PATIENT WITH SEVERE AORTIC STENOSIS: THE ROLE OF FRAILTY IN THE CHOICE OF TREATMENT (TAVI/SAVR)
Bibliographic record
Abstract
Abstract Background Surgical Aortic Valve Replacement (SAVR) and Transcatheter Aortic Valve Replacement (TAVI) are treatment options in patients with severe aortic stenosis. International guidelines suggest both traditional risk scores and a Comprehensive Geriatric Assessment (GCA) be determinants in treatment decision–making. The aim of the study is to identify the GCA parameters associated with the decision to perform TAVI instead of SAVR in elderly patients with severe aortic stenosis. Materials and Methods Between 2019 and 2022, 66 patients aged ≥75 years were referred to the Cardiology department of Padua Hospital for pre–operative evaluation, including CGA. The CGA consisted of functional status (Activities of Daily Living and Instrumental Activities of Daily Living Scales), cognitive impairment (Mini Mental State Examination, Montreal Cognitive Assessment), mood disorders (Geriatric Depression Scale), physical performance (Short Physical Performance Battery, 6–minute Walking Test, grip strength) and quality of life (Short–Form 36 items Health Survey). Results SAVR was proposed to 53% of patients and TAVI to 47%. The mean age was 78.9 ± 4.8 years and 47% were women. TAVI patients were older and had lower scores on both cognitive and motor performance tests than SAVR patients. The geriatric parameters associated with treatment decision–making between SAVR and TAVI were age (OR= 1.64, p < 0.04) and gait speed (OR= 0.09, p = 0.05). Conclusions Therapeutic decision–making in elderly patients with severe aortic stenosis is not only influenced by age, but also by a frailty index, gait speed. This could become a simple, quick and inexpensive routine test to effectively stratify patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".