Efficiency of the Robotic Platform in Improving the Rate of Sphincter Preservation in Patients With Mid and Low Rectal Cancer
Bibliographic record
Abstract
Background: The aim of this study was to investigate whether the robotic platform can have a positive impact on the rate of sphincter preservation in patients with rectal tumors, undergoing robotic total mesorectal excision (TME), in comparison with laparoscopic or open TME. We also analyzed and compared short-term outcomes. Methods: A prospectively collected robotic database was reviewed and compared with the trust and national data. Three groups were designed according to the surgical technique: open, laparoscopic and robotic. This includes all resections for mid and low rectal cancer which were performed with the robotic platform, over a period of 4 years, versus the trust data for the same period. Results: Two hundred ninety-seven patients with mid and low rectal cancers were analyzed. Demographics for the groups (gender, age, and body mass index) were similar but distance from anal verge was shorter in the robotic group (7 vs. 8.5 cm, P < 0.001). The percentage of abdominoperineal resection (APR) rate was significantly lower in the robotic group (13.5% vs. 39.6% vs. 52.4% for the open group, P < 0.001). Median length of stay, complication rate, and positive circumferential resection margin (CRM) rate for the robotic group were also statistically significantly lower than those for both laparoscopic and open groups. Conclusion: Robotic surgery for mid and low rectal cancer is safe and feasible, and could help surgeons perform ultra-low anterior resections, rather than APRs and save patients' sphincters. Positive CRM is low, which could lead to improved oncological outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".