Minimally Invasive Versus Open Treatment for Benign Sporadic Insulinoma Comparison of Short‐Term and Long‐Term Outcomes
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it