Prevalence and Impact of Prosthesis-Patient Mismatch Following Surgical Aortic Valve Replacement for Pure Aortic Regurgitation.
Bibliographic record
Abstract
BACKGROUND: Prosthesis-patient mismatch (PPM) is highly prevalent among patients undergoing aortic valve replacement (AVR) to treat aortic stenosis. Data regarding the prevalence and impact of PPM on left ventricular remodeling and outcomes in patients who have undergone surgical AVR to treat pure severe aortic regurgitation (AR) are, however, scarce. METHODS: A retrospective analysis was conducted of clinical and echocardiographic data acquired from 50 consecutive patients with pure severe AR, without evidence of significant coronary artery disease, who underwent AVR between 2004 and 2010 at the authors' institution. PPM was defined as a projected in vivo effective orifice area (EOA) 0.85 cm2/m2. RESULTS: The incidence of PPM was 16%, but no severe mismatch occurred. At a mean follow up of 52 ± 39 months, event-free survival (a composite of all-cause mortality and hospitalization for cardiovascular causes) was similar between patients with and without PPM (p = 0.73). Within seven days after surgery, mean reductions in indexed left ventricular end-diastolic diameter (LVEDD) and indexed left ventricular end-systolic diameter (LVESD) were similar between patients with and without PPM [4.4 mm/m2 versus 5.0 mm/m2; p = 0.67 and 1.6 mm/m2 versus 2.2 mm/m2; p = 0.35, respectively]. At follow up, no difference was observed for mean reductions in indexed LVEDD and indexed LVESD [6.9 mm/m2 versus 7.1 mm/m2; p = 0.91 and 4.1 mm/m2 versus 5.1 mm/m2; p = 0.57, respectively], and mean improvement in left ventricular ejection fraction (4.4% versus 5.1%; p = 0.87). CONCLUSIONS: PPM occurs less frequently in patients undergoing AVR for pure severe AR than for aortic stenosis, and seems to have a less significant impact on ventricular remodeling and outcomes.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".