Laparoscopic versus robotic pancreaticoduodenectomy: A NSQIP analysis comparing trends in patient selection and outcomes over 5-years
Bibliographic record
Abstract
BackgroundComparison of laparoscopic (LPD) and robotic (RPD) pancreaticoduodenectomy over time remains limited. This study aims to compare LPD and RPD and to describe the demographics and outcomes of patients undergoing MIS pancreaticoduodenectomy over 5-years.MethodsThe ACS-NSQIP (2016–2021) database was used to evaluate patients undergoing MIS pancreaticoduodenectomy comparing LPD versus RPD. Patient characteristics, and outcomes were compared and multivariable modelling evaluated factors associated with serious complications, and mortality. MIS approach, demographics, and outcomes were assessed yearly to evaluate trends over time.ResultsWe evaluated 1707 patients with 1148 (67.3 %) receiving RPD. Cohorts were similar with regards to demographic factors, however, patients undergoing RPD were less likely to be partially dependent (0.5 % vs. 1.6 %; p = 0.024), and more likely to receive neoadjuvant therapy (26.8 % vs. 21.7 %; p = 0.023).Bivariate analysis demonstrated similar operative duration (444.1 vs 429.9 min; p = 0.074), but shorter LOS (8.5 vs. 9.8 days; p < 0.001), and higher readmission rate (21.5 % vs. 15.6 %; p = 0.004) with RPD. Additionally, RPD required transfusion less often (10.5 % vs. 21.7 %; p < 0.001). Multivariable analysis demonstrated that LPD was not independently associated with serious complications (OR 1.27 p = 0.094) or mortality (OR 0.82, p = 0.611).Analysis of trends from 2016 to 2021 demonstrated similar patient selection and outcomes but a significant increase in MIS pancreaticoduodenectomy (281 to 428), primarily driven by an increase in RPD.ConclusionsComparing LPD and RPD there is no difference in serious complications or mortality. MIS pancreaticoduodenectomy has increased over the last 5 years but volumes remain small with similar demographics and outcomes over time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".