Late clinical outcomes after mechanical aortic valve replacement for aortic stenosis: old versus new prostheses
Bibliographic record
Abstract
BACKGROUND: The study aimed to evaluate the late clinical outcomes of new-generation mechanical valves for severe aortic stenosis (AS) compared with old mechanical valves. METHODS: We retrospectively reviewed data from 254 patients with severe AS, who underwent primary mechanical aortic valve replacement from 1995 to 2013. Patients were classified into two groups: old-valve group (n=65: 33 ATS standard, 32 Medtronic-Hall) and new-valve group (n=189: 113 St. Jude Regent, 46 On-X, 30 Sorin Overline). Median patient age was 58 years (Q1-Q3: 52-61). With propensity score matching based on demographic information, 56 patients in the old-valve group were matched with 177 patients in the new-valve group. The median follow-up duration was 91 months (Q1-Q3: 48-138). RESULTS: Cardiac-related mortality and hemorrhagic events were significantly lower in the new-valve group (P=0.047 and P=0.032, respectively). The median international normalized ratio (INR) at follow-up was significantly higher in the old-valve group [2.23, Q1-Q3: 2.14-2.35 (old-valve group); 2.08, Q1-Q3: 1.92-2.23 (new-valve group), P<0.001]. The incidence of prosthesis-patient mismatch (PPM) was significantly higher in the old-valve group (P<0.001). Multivariate analysis of the total population revealed that PPM was a significant risk factor for cardiac-related events [hazard ratio (HR) =5.279, 95% CI, 1.886-14.561, P=0.002] and showed higher trend of increasing mortality (HR =3.082, P=0.076). CONCLUSIONS: New mechanical prostheses showed a better hemodynamic performance and lower incidence of PPM. Anticoagulation strategy to lower the target INR in patients with new mechanical valves may improve late outcomes by reducing hemorrhagic events.
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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 | 0.001 |
| 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) | 0.001 | 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".