The Obesity Paradox Revisited: Is Obesity Still a Protective Factor for Patients With High Comorbidity Burden or High-Complexity Procedures?
Bibliographic record
Abstract
Objective: To investigate the relationship between obesity and postoperative mortality in the context of high procedural complexity and comorbidity burden. Background: The "obesity paradox" suggests better postoperative outcomes in patients with higher body mass index (BMI), despite obesity's associated health risks. Research remains scarce on the influence of procedural complexity and comorbidities on the obesity-postoperative mortality relationship. Methods: We performed an observational study of adult patients undergoing major surgery using the 2016 to 2019 National Surgical Quality Improvement Program database. The outcome was 30-day mortality. We first estimated the risk-adjusted effects of BMI on mortality across the full cohort via multivariable regression and restricted cubic spline models. Then, we investigated the subgroups stratified by procedural complexity and comorbidity burden using a modified Charlson Comorbidity Index (mCCI) and mortality probability. Results: Among 3,085,582 patients, 47% had obesity. There was a reverse J-shaped relationship between BMI and mortality in the full cohort, consistent with the obesity paradox. However, no difference in odds of mortality was observed in patients with obesity who underwent high-complexity procedures compared with normal BMI counterparts (BMI 30-34.9: odds ratio, 0.93 [95% confidence interval: 0.86-1.01]; BMI 35-39.9: 0.92 [0.83-1.03]; BMI ≥ 40: 0.94 [0.83-1.07]), and in patients with obesity with high comorbidity burden (mCCI ≥ 8 [BMI 30-34.9: 0.95 (0.77-1.16); BMI 35-39.9: 0.78, (0.60-1.02); BMI ≥ 40: 0.84 (0.63-1.12)] and top 3% mortality probability [BMI 30-34.9: 0.96 (0.90-1.02); BMI ≥ 40: 0.94 (0.86-1.01)]). Conclusion: Our findings suggest the existence of an obesity paradox in most adult surgical patients, yet the trend dissipates with high procedural complexity and comorbidity burden.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".