FRAILTY ASSESSMENT AND 6-MONTH FUNCTIONAL STATUS AND MORTALITY AFTER AORTIC VALVE REPLACEMENT
Bibliographic record
Abstract
Preoperative frailty assessment may be useful to predict functional status and mortality in older adults undergoing aortic valve replacement (AVR). We conducted a single-center prospective cohort study to compare frailty phenotype versus comprehensive geriatric assessment-based frailty index (CGA-FI) in predicting 6-month functional status and mortality after transcatheter and surgical AVR. Between February 2014 and December 2015, we assessed frailty phenotype and CGA-FI in patients 70 years or older who underwent transcatheter AVR (N=124) or surgical AVR (N=88). Telephone interviews were performed at 1, 3, and 6 months to assess the ability to perform 22 functional activities. The composite poor clinical outcome, defined as death or functional decline with the New York Heart Association class 3 or 4, occurred in 31 (26%) of 120 transcatheter AVR patients (drop-out: 4) and 8 (11%) of 76 surgical AVR patients (drop-out: 12). The risk of poor clinical outcome did not differ between patients with and without frailty phenotype undergoing transcatheter AVR (24 of 96 [25%] versus 7 of 24 [29%]; p=0.68) and surgical AVR (5 of 27 [19%] versus 3 of 49 [6%]; p=0.11). However, the risk increased with CGA-FI (≤0.20, 0.21–0.40, >0.40) in transcatheter AVR (1 of 12 [8%], 12 of 64 [19%], 18 of 44 [41%]; p=0.01) and surgical AVR (1 of 29 [3%], 5 of 42 [12%], 2 of 5 [40%]; p=0.04). In conclusion, CGA-FI predicts 6-month functional status and mortality after transcatheter and surgical AVR, but frailty phenotype has a limited role in this population with high prevalence of frailty.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".