International multicentre validation of the left pancreatectomy pancreatic fistula prediction models and development and validation of the combined DISPAIR-FRS prediction model
Bibliographic record
Abstract
BACKGROUND: Every fifth patient undergoing left pancreatectomy develops a postoperative pancreatic fistula (POPF). Accurate POPF risk prediction could help. Two independent preoperative prediction models have been developed and externally validated: DISPAIR and D-FRS. The aim of this study was to validate, compare, and possibly update the models. METHODS: Patients from nine high-volume pancreatic surgery centres (8 in Europe and 1 in North America) were included in this retrospective cohort study. Inclusion criteria were age over 18 years and open or minimally invasive left pancreatectomy since 2010. Model performance was assessed with discrimination (receiver operating characteristic (ROC) curves) and calibration (calibration plots). The updated model was developed with logistic regression and internally-externally validated. RESULTS: Of 2284 patients included, 497 (21.8%) developed POPF. Both DISPAIR (area under the ROC curve (AUC) 0.62) and D-FRS (AUC 0.62) performed suboptimally, both in the pooled validation cohort combining every centre's data and centre-wise. An updated model, named DISPAIR-FRS, was constructed by combining the most stable predictors from the existing models and incorporating other readily available patient demographics, such as age, sex, transection site, pancreatic thickness at the transection site, and main pancreatic duct diameter at the transection site. Internal-external validation demonstrated an AUC of 0.72, a calibration slope of 0.93, and an intercept of -0.02 for the updated model. CONCLUSION: The combined updated model of DISPAIR and D-FRS named DISPAIR-FRS demonstrated better performance and can be accessed at www.tinyurl.com/the-dispair-frs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".