The effects of body mass index on outcomes for patients undergoing surgical aortic valve replacement
Bibliographic record
Abstract
BACKGROUND: Most of the studies of obesity and postoperative outcome have looked predominantly at coronary artery bypass grafting with fewer focused on valvular disease. The purpose of this study was to compare the outcomes of patients undergoing aortic valve replacement stratified by body mass index (BMI, kg/m^2). METHODS: The Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry captured 4780 aortic valve replacements in Alberta, Canada from January 2004 to December 2018. All recipients were stratified by BMI into five groups (BMI: < 20, 20-24.9, 25-29.9, 30-34.9, and > = 35). Log-rank test and Cox regression were used to examine the crude and adjusted survival differences. RESULTS: Intra-operative clamp time and pump time were similar among the five groups. Significant statistical differences between groups existed for the incidence of isolated AVR, AVR and CABG, hemorrhage, septic infection, and deep sternal infection (p < 0.05). While there was no significant statistical difference in the mortality rate across the BMI groups, the underweight AVR patients (BMI < 20) were associated with increased hazard ratio (1.519; 95% confidence interval: 1.028-2.245) with regards to all-cause mortality at the longest follow-up compared with normal weight patients. CONCLUSION: Overweight and obese patients should be considered as readily for AVR as normal BMI 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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".