Robotic versus laparoscopic abdominoperineal resections for low rectal cancer: A single‐center randomized controlled trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Robotic surgery for rectal cancer is gaining popularity, but persuasive evidence on reducing surgical trauma is still lacking. This study compared robotic and laparoscopic abdominoperineal resections (APRs) for the risk of postoperative complications in low rectal cancer. METHODS: Between December 2013 and 2016, patients with rectal cancer ≤5 cm from anal verge, cT1-T3 N0-1, or ycT1-T3 Nx stage, and no distant metastases were enrolled in a single-center, randomized, controlled trial. Eligible patients were randomly allocated to robotic or laparoscopic APRs at 1:1 ratio. The primary outcome was 30-day postoperative complication rate (Clavien-Dindo grade II or higher) of the intent-to-treat population. The trial registration number is NCT01985698 (http://www. CLINICALTRIALS: gov). RESULTS: Totally 347 eligible patients were enrolled: 174 in robotic and 173 in laparoscopic group. Robotic APRs significantly reduced postoperative complication rate (13.2% vs. 23.7%, p = 0.013), also reduced open conversion rate (0% vs. 2.9%, p = 0.030), intraoperative hemorrhage (median, 100 vs. 130 ml; p < 0.001), 30-day readmission rate (2.3% vs. 6.9%; p = 0.044), postoperative hospital stay (median, 5.0 vs. 7.0 days; p < 0.001), and improved urinary and sexual function. No significant difference was observed in long-term oncological outcomes. CONCLUSIONS: Compared with laparoscopic APRs, robotic APRs significantly reduced surgical trauma and promoted postoperative recovery.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".