Nomogram for prediction of adverse events after lumen‐apposing metal stent placement for drainage of pancreatic fluid collections
Bibliographic record
Abstract
OBJECTIVES: To generate a prognostic model based on a nomogram for adverse event (AE) prediction after lumen-apposing metal stents (LAMS) placement in patients with pancreatic fluid collections (PFC). METHODS: Data from a large multicenter series of PFCs treated with LAMS placement were retrieved. AE (overall and excluding mild events) prediction was calculated through a logistic regression model and a nomogram was created and internally validated after bootstrapping. Results were expressed in terms of odds ratio (OR) and 95% confidence interval (CI). Discrimination was assessed by c-statistics and calibrated by comparing deciles of predicted and observed ORs. RESULTS: Overall, 516 patients were included (males 68%, mean age 61.6 ± 15.2 years). PFCs were predominantly walled-off necrosis (52.1%). Independent predictors of AE occurrence were injury of main pancreatic duct (OR in the case of leak 2.51, 95% CI 1.06-5.97, P = 0.03; OR in the case of complete disruption 2.61, 1.53-4.45, P = 0.01), abnormal vessels (OR in the case of perigastric varices 2.90, 1.31-6.42, P = 0.008; OR in the case of pseudoaneurysm 2.99, 1.75-11.93, P = 0.002), using a multigate technique (OR 3.00, 1.28-5.24; P = 0.05), and need of percutaneous drainage (OR 2.81, 1.03-7.65, P = 0.04). By nomogram, a score beyond 200 points corresponded to a 50% probability of AE occurrence. The model was confirmed even when excluding mild AEs and it showed optimal discrimination (c-index 76.8%, 95% CI 74-79), confirmed after internal validation. CONCLUSION: Patients with preprocedural evidence of pancreatic duct leak/disruption, vessel alteration, requiring percutaneous drainage or a multigate technique are at higher risk for AE.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".