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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".