Impact of aortic stenosis severity and its interaction with prosthesis-patient mismatch on operative mortality following aortic valve replacement.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: The optimal timing of aortic valve replacement (AVR) in patients with severe aortic stenosis (AS) is a source of debate. Moreover, it has been shown previously that prosthesis-patient mismatch (PPM) is an independent predictor of operative mortality after AVR. The study aim was to assess the effect of the preoperative severity of AS and its interaction with PPM with respect to operative mortality after AVR. METHODS: The data were analyzed from 2,104 consecutive patients who had undergone AVR for severe AS. The patients were allocated to tertiles according to their preoperative indexed aortic valve area (AVAi) as: < 0.35 cm2/m2, 0.35 to 0.43 cm2/m2, and > 0.43 cm2/m2. PPM was defined as a projected postoperative indexed effective orifice area (EOAi) of the implanted prosthesis < 0.85 cm2/m2. RESULTS: The operative mortality was 5.7% (n = 120). On multivariate analysis, an independent association was identified between the preoperative severity of AS and operative mortality (odds ratio [OR] = 2.00, p = 0.03 for AVAi < 0.35 cm2/m2; OR = 1.39, p = 0.32 for AVAi 0.35-0.43 cm2/m2). Notably, the impact of PPM was more important in patients with more severe AS (p = 0.046 for AVAi x EOAi interaction). CONCLUSION: The study results confirmed that very severe AS (AVAi < 0.35 cm2/m2) is independently associated with operative mortality after AVR. The results also emphasized the importance of avoiding PPM in these patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".