Prediction model based on contrast-enhanced computed tomography images and clinical indicators for the prognosis of pancreatic necrosis in acute pancreatitis
Bibliographic record
Abstract
The clinical outcomes of acute necrotizing pancreatitis (ANP) including necrotic tissue absorption, persistent walled-off necrosis (WON) formation, and infectious pancreatic necrosis (IPN) require different treatment modalities. In this prospective observational study, patients with ANP admitted to our hospital between January 2021 and December 2022 underwent contrast-enhanced computed tomography (CECT) and clinical tests within 24 hours of admission and were followed up for 6 months. CECT images of the pancreas were automatically segmented using deep learning (DL) model, and 3D ResNet DL and logistic regression (LR) models were developed using CECT images and selected clinical indicators, respectively. Prediction models were obtained via the integration of the DL and LR models, and comparison of their respective performances. Of the 133 patients with ANP, absorption of necrotic tissues, persistent WON, and IPN were found in 45.86, 30.83, and 23.31% of the patients, respectively. For pancreatic segmentation, the Attention U-Net model performed better than the U-Net model. Blood glucose, urea nitrogen, lactate dehydrogenase, and C-reactive protein levels were then used to construct an LR model with .714 accuracy. The accuracies of the 3D ResNet models using manually and automatically segmented pancreatic images were .821 and .750 initially, respectively, and .857 and .786 when combined with LR, respectively. The model developed in this study may be clinically applied to improve the accuracy of ANP prognosis prediction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".