Obesity: An Independent Predictor of Acute Renal Failure After General Surgery
Bibliographic record
Abstract
Background Half of Americans will have obesity, and a quarter will have severe obesity by the year 2030. Postoperative acute renal failure (ARF) is associated with increased morbidity and mortality. Given the increase in the number of patients with obesity undergoing elective surgery, we investigated the relationship between obesity and postoperative ARF after elective general surgery procedures. Methods We performed a retrospective cohort study of patients in the 2015-2019 National Surgical Quality Improvement Program database who underwent elective general surgery procedures. The primary outcome was the presence of postoperative ARF. The patient body mass index (BMI) was categorized as normal (BMI 18.5-24.9), overweight (BMI 25-29.9), obesity class 1 and 2 (BMI 30-39.9), severe obesity (BMI 40-49.9), and extreme obesity (BMI³50). Descriptive statistics and unadjusted comparisons were performed for patients who developed postoperative ARF and those who did not. Multivariable regression analyses were used to model BMI categories and postoperative ARF, adjusting for patient- and surgical-level covariates. Results Among 424,527 patients included in the study, 3638 patients (0.8%) developed ARF. Patients who developed ARF were older, had a higher BMI, and had more serious comorbidities. After risk adjustment, there was a stepwise rise in odds of developing postoperative ARF with increasing BMI categories compared to normal BMI: (overweight: OR 1.11 (95% CI 1.0-1.23), obesity class 1 and 2: OR 1.32 (95% CI 1.2-1.46), severe obesity: OR 1.45 (95% CI 1.27-1.66), and extreme obesity: OR 1.78 (95% CI 1.47-2.15)). Conclusion Obesity is independently associated with ARF after elective general surgery procedures.
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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 | 0.001 |
| 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".