Poor mobility predicts adverse outcome better than other frailty indices in patients undergoing transcatheter aortic valve implantation
Bibliographic record
Abstract
BACKGROUND: Surgical risk scoring systems are poor at predicting outcome in patients undergoing transcatheter aortic valve implantation (TAVI). Frailty indices might more accurately predict outcome. AIMS: To examine multiple frailty indices as markers of performance to see whether they predict outcomes both in the shorter (30 days) and longer terms (5 years) in patients who have undergone TAVI. METHODS: Frailty indices (Mobility; Brighton Mobility Index, New York Heart Association (NYHA), Karnofsky Performance Index, Canadian Study Health Association (CSHA) clinical frailty scale, and Katz Index of Dependence) were assessed in 312 consecutive TAVI patients. Mortality tracking was obtained from the Office of National Statistics as of May 2014. RESULTS: Mean age was 81.2 ± 7.0 years; 53.2% were male. Mean Logistic EuroSCORE and STS were 17.4 ± 9.4 and 4.6 ± 2.8, respectively. Mean peak aortic valve gradient and aortic valve area were 79.1 ± 28.0 mm Hg and 0.72 ± 0.25 cm(2) , respectively. 30-day mortality was 4.8%; long-term mortality (maximum 5.8 years, mean 2.2 ± 1.5 years) was 25.3%. Both univariate and multivariate analyses confirmed poor mobility (defined as severe impairment of mobility secondary to musculoskeletal or neurological dysfunction (Euroscore II risk)), as the best predictor of adverse outcome over both the short-term (OR 4.03, 95% CI (1.36-11.96), P = 0.012 (30 days)) and longer term (OR 2.15, 95% CI (1.33-3.48), P = 0.002, (2.2 ± 1.5 years.)). CONCLUSION: Poor mobility predicts worse survival among patients undergoing TAVI, both in the shorter and longer terms. Our data suggest that mobility impairment, of either neurological or musculoskeletal etiology, is an appropriate screening measure when considering patients for TAVI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.004 |
| 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".