P4495Psoas muscle area and volume and frailty scoring as predictors of outcomes after transcatheter aortic valve implantation
Bibliographic record
Abstract
Background: The assessment of frailty rely mostly on physical/functional performance tests or subjective questionnaires which are less feasible in very frail patients comparing to sarcopenia, defined as low muscle mass, that can be assessed objectively and relatively quickly by imaging modalities. Purpose: We aimed to determine the long-term predictive value of different frailty scores and objective assessment of sarcopenia in patients undergoing transcatheter aortic valve implantation (TAVI). Methods: Frailty indices according to VARC-2 recommendations [5-meter walk test (5MWT) and hand grip strength] as well as other available scales of frailty [Katz index, elderly mobility index (EMS), Canadian Study of Health and Aging (CSHA) scale, Identification of Seniors at Risk (ISAR) scale] were assessed at baseline. Sarcopenia was evaluated with psoas muscle area (PSA) and volume (PSV) using CT scans. The primary endpoint was 12-month all-cause mortality. Results: We enrolled 153 TAVI patients with analyzable CT scans and complete frailty data. Median of PSA normalized for body surface area (BSA) was 2581.1 (2214.9–2654.9) mm2/m2, and median of normalized PSV was 338.8 (288.1–365.6) cc/m2. According to 5MWT 13.7% were frail, EMS scale – 5.2%, CSHA scale - 11.1%, Katz index - 12.4% patients, hand grip test - 4.6%, and ISAR scale – 28.7%. At 12 months, all-cause mortality and new-onset atrial fibrillation were highest in the lowest tertile of normalized PSA. In the ROC analysis, all the tested frailty indices, as well as PSA and PSV, were good predictors of 12-month all-cause mortality after TAVI with the highest AUC value for PSA and PSV normalized for BSA.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".