External validation of ISGPS two-factor, four-tier classification for the prediction of pancreatic fistula after pancreaticoduodenectomy
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study is to externally validate the International Study Group of Pancreatic Surgery (ISGPS) risk classification for postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy. METHODS: A validation study was performed in consecutive patients undergoing pancreatoduodenectomy (PD) for all indications from January 2008 to December 2024. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve and calibration plots. RESULTS: Overall, 591 patients were included in the present study. POPF was observed in 16.2 % of patients. The distribution of patients according to the ISGPS risk categories were: A (32.5 %), B (7.8 %), C (24 %), and D (35.7 %) with corresponding POPF rates of 9.4 %, 15.2 %, 12 %, and 25.6 %. There was no difference in the rate of POPF between risk categories B and C (15.2 % vs 12 %, P = 0.613). ISGPS 3-tier classification showed better distribution of patients in each risk category (A: 32.5 %, B: 31.8 %, C: 35.7 %). Area under the curve (AUC) of ROC for the ISGPS 4-tier and 3-tier classification were 0.633, and 0.636 respectively (p = 0.945). All three models demonstrate suboptimal calibration, with predicted risks substantially underestimating the true incidence of POPF, and predicted probabilities failing to fully differentiate between low- and high-risk individuals. CONCLUSION: This external validation study showed moderate model discrimination of ISGPS 4-tier classification. ISGPS 3-tier classification is as predictive as ISGPS 4-tier classification.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".