Surgical and Oncological Outcomes of Minimally Invasive Left Pancreatectomy for Pancreatic Cancer: Robotic vs. Laparoscopic Approach
Bibliographic record
Abstract
Objective: This study compares the surgical and oncological outcomes of minimally invasive robotic (RLP) and laparoscopic (LLP) left pancreatectomy in pancreatic cancer (PC) patients. Methods: Data from patients who underwent minimally invasive left pancreatectomy between 2013 and 2023 were analyzed. Two groups were identified: RLP and LLP. Perioperative outcomes were compared, including operative time, blood loss, conversion rate, and postoperative complications. Oncological outcomes included margin status, lymph node retrieval, lymph node status, overall survival (OS), and disease-free survival (DFS). Results: Fifty-four patients were divided into the LLP (n = 39) and RLP (n = 15) groups. The median operative time was shorter for LLP than RLP [260 min vs. 366 min, p = 0.007]. Blood loss and conversion rates were comparable (p > 0.05). In the LLP group, significantly more lymph nodes were harvested (29 vs. 19, p = 0.05), and a higher percentage of positive lymph nodes was noted (72% vs. 40%, p = 0.033). No significant difference was found in the R0 resection status (82% vs. 73%, p = 0.358). After a median follow-up of 26 months, OS (23 months vs. 34 months, p = 0.812) and DFS (17 months vs. 16 months, p = 0.635) were similar. Conclusion: RLP provides outcomes identical to LLP in treating body–tail pancreatic cancer, with further studies needed to confirm its long-term oncological efficacy.
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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 | 0.002 |
| 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 | 0.001 |
| 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".