External Validation of the International Study Group for Pancreatic Surgery Complexity Grading System for Minimally Invasive Pancreatoduodenectomy
Bibliographic record
Abstract
OBJECTIVE: To validate the International Study Group for Pancreatic Surgery (ISGPS) complexity grading system for minimally invasive pancreaticoduodenectomy (MIPD). BACKGROUND: Although concerns about patient safety persist, MIPD is gaining popularity. The ISGPS recently introduced a difficulty grading system to improve patient selection by aligning procedural complexity with surgeon and center expertise. METHODS: Data from MIPD cases reported in the IGOMIPS registry (October 2019-February 2024) were analyzed, with severe postoperative complications as the primary outcome. Logistic regression was used to identify risk factors for complications. RESULTS: Of the 771 MIPD cases, 426 (55.3%) were analyzed. A pancreatic duct size ≤3 mm was the only significant risk factor for severe complications (odds ratio = 2.22, P = 0.0001). Most cases (n = 255; 59.9%) were classified as grade C complexity, whereas 22 (5.1%) were classified as grade A. Severe postoperative complications increased with complexity (grade A, 31.8%; grade B, 36.3%; grade C, 48.6%; P = 0.0091). For grade A complexity, the outcomes were consistent across surgeons and centers. Grade B outcomes were similar between grade B and C centers but superior to grade A centers. In grade C cases, outcomes were comparable between grade A and B centers, with improvements at grade C centers. Grade A ISGPS experience correlated strongly with mismatches between planned and performed procedures (grade A, 15.0%; grade B, 3.0%; grade C, 3.1%; P < 0.0001), including total pancreatectomy (grade A, 11.5%; grade B, 1.2%; grade C, 3.1%; P = 0.0005). CONCLUSIONS: The ISGPS complexity grading system effectively predicted MIPD outcomes, supporting better patient selection and alignment of complexity with surgical expertise.
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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 | 0.031 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".