Scores for Prediction of Fistula after Pancreatoduodenectomy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/AIM: Different scoring systems to predict the occurrence of postoperative pancreatic fistula (POPF) after pancreatoduodenectomy have been described, but the considered risk factors often suffer subjective scaling. The aim of this review is to evaluate and compare all published risk metrics predictive of POPF. METHODS: All existing scores were retrieved by literature web search. Inclusion criteria were ISGPF classification of POPF and the development of a risk score metric. RESULTS: From a total of 286 publications, 10 studies were selected. Most of them were retrospective and single center. The models considered a median number of 3 items (range from 2 to 5); in 5 of 10 trials only pre or intraoperative variables were included. The median number of patients/study was 186 (IQR 111.1-229.0). External validation was performed in 6 of 10 studies. The most recurrent items were abdominal fat (4/10), main pancreatic duct diameter (in 4/10), and pancreatic texture (3/10). CONCLUSION: POPF risk estimation should be easy, accurate, and objective. It should consider preoperative patient-related and gland-related features, and intraoperative events. None of the published systems completely adhere to these principles. Large heterogeneous multicentric validations should be endorsed, to account for the case-mix and evaluate the reproducibility of each scoring system.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".