2-046 Mass index classifications and their associations with short-term outcomes of surgical aortic valve replacement
Bibliographic record
Abstract
Introduction Several cardiovascular procedures, like transcatheter aortic valve implantation(TAVI), have shown an obesity paradox. However, limited data exists regarding the impact of body mass index (BMI) on patients undergoing surgical aortic valve replacement(SAVR), particularly among underweight cases. This study aims to conduct a BMI- stratified analysis of short-term outcomes following SAVR. Methods Procedures of SAVR were extracted using the National Inpatient Sample(2016–2022). We stratified our cohort based on BMI into underweight, normal, overweight, and obese. Keeping the normal BMI cohort as a reference, we evaluated the differences in short-term outcomes. Results In this study, we considered 3150(3.7%) patients with normal BMI, while there were 2300(2.7%), 12325(14.3%), and 68425(79.4%) classified as underweight, overweight, and obese respectively. We found that underweight patients had higher odds of sepsis, post-procedural respiratory failure(PPRF), cardiogenic shock(CS), acute kidney injury requiring hemodialysis(AKI+HD), and all-cause mortality than normal BMI cases. Meanwhile, obese and overweight patients expressed lower odds of sepsis, major bleeding, and CS than the normal BMI group. Furthermore, overweight patients also had lower odds of PPRF, and AKI+HD (vs. normal BMI), while obese patients had lower odds of post-procedural cerebrovascular event(PPCE)(vs. normal BMI). Conclusion In patients undergoing SAVR, obese and overweight individuals paradoxically experienced a lower risk for several complications, while underweight patients faced a higher risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".