Comparison of robotic and laparoscopic colorectal resections with respect to 30-day perioperative morbidity
Bibliographic record
Abstract
BACKGROUND: Robotic surgery has emerged as a minimally invasive alternative to traditional laparoscopy. Robotic surgery addresses many of the technical and ergonomic limitations of laparoscopic surgery, but the literature regarding clinical outcomes in colorectal surgery is limited. We sought to compare robotic and laparoscopic colorectal resections with respect to 30-day perioperative outcomes. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was used to identify all patients who underwent robotic or laparoscopic colorectal surgery in 2013. We performed a logistic regression analysis to compare intraoperative variables and 30-day outcomes. RESULTS: There were 8392 patients who underwent laparoscopic colorectal surgery and 472 patients who underwent robotic colorectal surgery. The robotic cohort had a lower incidence of unplanned intraoperative conversion (9.5% v. 13.7%, p = 0.008). There were no significant differences between robotic and laparoscopic surgery with respect to other intraoperative and postoperative outcomes, such as operative duration, length of stay, postoperative ileus, anastomotic leak, venous thromboembolism, wound infection, cardiac complications and pulmonary complications. On multivariable analysis, robotic surgery was protective for unplanned conversion, while male sex, malignancy, Crohn disease and diverticular disease were all associated with open conversion. CONCLUSION: Robotic colorectal surgery has comparable 30-day perioperative morbidity to laparoscopic surgery and may decrease the rate of intraoperative conversion in select 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".