Comparison of Laparoscopic and Robotic Lateral Lymph Node Dissection for Rectal Cancer: A Systematic Review and Meta-analysis of Short- and Long-term Outcomes
Bibliographic record
Abstract
AIM: The importance of lateral lymph node dissection (LLND) for advanced low rectal cancer is still questioned, but selected patients might benefit from this procedure. The purpose of this study was to compare robotic LLND (R-LLND) versus laparoscopic LLND (L-LLND) to identify the safety, feasibility, and advantages of R-LLND. METHODS: PubMed, Scopus, and Cochrane databases were searched for studies assessing the benefit of R-LLND over L-LLND. Pooled odds ratios (OR) and weighted mean difference (WMD) were obtained using models with random effects. The risk of bias was evaluated with the Newcastle-Ottawa scale. RESULTS: Six studies were included in our analysis for a total of 652 patients (316 robotic and 336 laparoscopic). The R-LLND group had a longer operative time (WMD 60.46, p = 0.02) and less blood loss (WMD –22.33, p = 0.01). Differences were found in the postoperative length of stays (7 days ± 1.2 and 14 ± 5.2 versus 7 days ± 0.3 and 16 ± 18.5, WMD –1.30, p = 0.03) and in the mean time to regular diet (3 days ± 0.5 and 5 ± 2.3 versus 3 days ± 1.2 and 6 ± 3.8, WMD –0.60 p = 0.01); a slightly higher number of harvested lateral lymph nodes was present in the L-LLND group (WMD 1.23, p = 0.02). CONCLUSIONS: Our work demonstrates a slight benefit from the robotic approach when performing LLND in terms of intra- and peri-operative outcomes, despite not reaching statistical significance a trend in favor of robotic surgery is evident in almost all the analyzed topic.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".