Minimally Invasive Versus Open Treatment for Benign Sporadic Insulinoma Comparison of Short‐Term and Long‐Term Outcomes
Bibliographic record
Abstract
BACKGROUND: Benign insulinoma is the most common functioning neuroendocrine tumor of the pancreas, and its incidence is estimated at 0.4%. The treatment of choice is organ-preserving resection. The aim of this study was to compare short-term and long-term outcomes of minimally invasive laparoscopic or robotic enucleation (MIC-EN) and open enucleation (O-EN) for sporadic benign insulinoma. METHODS: A retrospective bi-institutional analysis of 71 patients who underwent an enucleation for sporadic benign insulinoma between 2003 and 2016 was performed. Patients were analyzed according to intention-to-treat principle. RESULTS: Fifteen (21%) patients underwent MIC-EN (three robotic and 12 laparoscopic) and 56 (79%) patients O-EN. In all MIC-EN patients, the insulinoma was localized by preoperative imaging compared to only 62.5% (35 of 56) patients in the O-EN group (p = 0.005). Three of the MIC-EN patients (20%) with insulinomas in the pancreatic head had to undergo a conversion. Excluding conversions, MIC-EN procedures were shorter (145 vs 180, p = 0.036) compared to O-EN surgery. Late complications and pathological data did not differ between groups, excluding margin status R1 MIC-EN (26.7%) compared to O-EN (10.7%, p = 0.115). After a median follow-up of 75 (range 1-151) months, all patients were alive, but four (5.6%) patients (one after MIC-EN and three after O-EN) developed a functional recurrence. No patient with a R1 resection had a disease recurrence. CONCLUSIONS: MIC-EN for benign sporadic insulinoma is a safe procedure with at least similar short-term and long-term postoperative outcomes as the open technique. Thus, preoperatively localized benign insulinoma should be approached laparoscopically, if technically feasible.
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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) | 0.001 | 0.001 |
| 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".