401 consecutive minimally invasive distal pancreatectomies: lessons learned from 20 years of experience
Bibliographic record
Abstract
BACKGROUND: This study aimed to discuss and report the trend, outcomes, and learning curve effect after minimally invasive distal pancreatectomy (MIDP) at two high-volume centres. METHODS: Patients undergoing MIDP between January 1999 and December 2018 were retrospectively identified from prospectively maintained electronic databases. The entire cohort was divided into two groups constituting the "early" and "recent" phases. The learning curve effect was analyzed for laparoscopic (LDP) and robotic distal pancreatectomy (RDP). The follow-up was at least 2 years. RESULTS: The study population included 401 consecutive patients (LDP n = 300, RDP n = 101). Twelve surgeons performed MIDP during the study period. Although patients were more carefully selected in the early phase, in terms of median age (49 vs. 55 years, p = 0.026), ASA class higher than 2 (3% vs. 9%, p = 0.018), previous abdominal surgery (10% vs. 34%, p < 0.001), and pancreatic adenocarcinoma (PDAC) (7% vs. 15%, p = 0.017), the recent phase had similar perioperative outcomes. The increase of experience in LDP was inversely associated with the operative time (240 vs 210 min, p < 0.001), morbidity rate (56.5% vs. 40.1%, p = 0.005), intra-abdominal collection (28.3% vs. 17.3%, p = 0.023), and length of stay (8 vs. 7 days, p = 0.009). Median survival in the PDAC subgroup was 53 months. CONCLUSION: In the setting of high-volume centres, the surgical training of MIDP is associated with acceptable rates of morbidity. The learning curve can be largely achieved by several team members, improving outcomes over time. Whenever possible resection of PDAC guarantees adequate oncological results and survival.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".