Late Clinical Outcomes of Balloon-Expandable Valves in Small Annuli
Bibliographic record
Abstract
BACKGROUND: Short-term clinical outcomes after transcatheter aortic valve replacement (TAVR) are similar in individuals with small or large annuli. The longer term impact of prosthesis-patient mismatch (PPM) and mean gradient (MG) post-TAVR in these patients remains controversial. OBJECTIVES: The aim of this study was to investigate 5-year outcomes in patients with small vs large annuli. METHODS: ) annular size. The primary endpoint was a composite of all-cause death, disabling stroke, or heart failure hospitalization. In addition, the relationships between both PPM and post-TAVR MG and clinical outcomes were analyzed. RESULTS: ). Patients with small annuli were older (age 79.6 ± 7.1 years vs 78.7 ± 7.8 years; P = 0.047), were more often female (75.0% vs 16.2%; P < 0.0001), had higher baseline Society of Thoracic Surgeons scores (4.3% ± 1.93% vs 4.0% ± 1.93%; P < 0.0001), and had higher left ventricular ejection fractions (66.3% ± 15.82% vs 59.7% ± 13.68%; P < 0.0001). Primary endpoint rates were similar at 1 year (7.8% vs 8.0%; P = 0.94) and 5 years (36.3% vs 35.8%; P = 0.83). Bioprosthetic valve failure was infrequent at 5 years in both groups (2.9% vs 2.1%; P = 0.46). Among female patients, outcomes were similar for small vs large annuli (primary endpoint; 33.6% vs 34.2%; P = 0.90). Among patients with small annuli, there was no association between 5-year outcomes and any severity of PPM (P = 0.22) or 30-day MG (P for nonlinearity = 0.96). CONCLUSIONS: Five-year clinical outcomes were excellent and comparable between patients with small vs large aortic annuli. Outcomes in patients with small annuli were not affected by 30-day MG or PPM.
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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 | 0.000 | 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 | 0.001 |
| 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".