The impact of obesity on postoperative outcomes following surgery for colorectal cancer: analysis of the National Inpatient Sample 2015–2019
Bibliographic record
Abstract
BACKGROUND: The global burden of obesity has reached epidemic proportions, placing great strain on the North American healthcare system. We designed a retrospective cohort database study comparing postoperative morbidity and healthcare resource utilization between patients living with and without obesity undergoing surgery for colorectal cancer. METHODS: ). Propensity score matching (PSM) with 1:1 nearest-neighbour matching was performed according to demographic, operative, and hospital characteristics. The primary outcome was postoperative morbidity. Secondary outcomes included system-specific postoperative complications, postoperative mortality, length of stay, total admission healthcare cost, and post-discharge disposition. McNemar's and Wilcoxon matched pairs signed rank tests were performed. RESULTS: After PSM, 7565 non-obese and 7565 obese patients were included. Patients with obesity had a 10% increase in relative risk of overall in-hospital postoperative morbidity (23.1% versus 25.6%, P = 0.0015) and a $4564 increase in hospitalization cost ($70 248 USD versus $74 812 USD, P = 0.0004). Patients with obesity were more likely to require post-operative ICU admission (5.0% versus 8.0%, P < 0.0001) and less likely to be discharged home after their index operation (68.3% versus 64.2%, P = 0.0022). CONCLUSION: Patients with obesity undergoing surgery for colorectal cancer may be at an increased risk of in-hospital postoperative morbidity. They may also be more likely to have increased hospitalization costs, post-operative ICU admissions, and to not be discharged directly home. Preoperative optimization via weight loss strategies should be further explored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".