Why the categorization of indexed effective orifice area is not justified for the classification of prosthesis–patient mismatch
Bibliographic record
Abstract
OBJECTIVES: Although the impact of prosthesis-patient mismatch (PPM) on survival has been widely studied, there has been little debate about whether the current definition of PPM truly reflects hemodynamic obstruction. This study aimed to validate the categorization of indexed effective orifice area (EOAi) for the classification of PPM. METHODS: In total, 2171 patients who underwent aortic valve replacement with a surgical stented bioprosthesis in 5 trials (CoreValve US High-Risk, SURTAVI [Surgical Replacement and Transcatheter Aortic Valve Implantation Trial], Evolut Low Risk, PERIGON [PERIcardial SurGical AOrtic Valve ReplacemeNt] Pivotal Trial for the Avalus valve, and PERIGON Japan) were used for this analysis. The echocardiographic images at the 1-year follow-up visit were evaluated to explore the association between EOAi and mean aortic gradient and its interaction with other patient characteristics, including obesity. In addition, different criteria of PPM were compared with reflect elevated mean aortic gradients (≥20 mm Hg). RESULTS: A relatively smaller exponential decay in mean aortic gradient was found for increasing EOAi, as the slope on the log scale was -0.83 versus -2.5 in the publication from which the current cut-offs for PPM originate. The accuracy of the American Society of Echocardiography, Valve Academic Research Consortium-2, and European Association of Cardiovascular Imaging definitions of PPM to reflect elevated mean aortic gradients was 49%, 57%, and 57%, respectively. The relation between EOAi and mean aortic gradient was not significantly different between obese and non-obese patients (P = .20). CONCLUSIONS: The use of EOAi thresholds to classify patients with PPM is undermined by a less-pronounced exponential relationship between EOAi and mean aortic gradient than previously demonstrated. Moreover, recent adjustment for obesity in the definition of PPM is not supported by these data.
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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 | 0.077 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".