Nomogram for prediction of adverse events after lumen‐apposing metal stent placement for drainage of pancreatic fluid collections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| 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 it