Factors affecting technical success of endoscopic retrograde cholangiopancreatographic outcomes in patients with surgically altered foregut anatomy: a retrospective study
Bibliographic record
Abstract
Introduction: Patients with surgically altered gastrointestinal anatomy undergoing endoscopic retrograde cholangiopancreatography (ERCP) pose challenges due to anatomical distortions. Various patient and endoscopic factors, such as sex and positioning, may impact procedural success. It is unclear how these factors may impact the technical success of ERCP among patients with altered anatomy. Objective: We aimed to determine the patient and endoscopic factors that were associated with technical success of ERCP. Methods: We conducted a retrospective single-centre study using data from 2010 to 2020 that included patients with hepaticojejunostomy, Roux-en-Y anastomosis, Billroth-1, or Billroth-2 anatomy at a single tertiary care centre in Toronto, Canada. We extracted data from a database. The primary outcome was technical success of the ERCP, defined as successful navigation to the papilla or surgical anastomosis, selective cannulation and cholangiography or pancreatography. Penalized logistic regression with elastic net regularization was used to identify significant predictors of technical success. Effect size was odds ratio with 95% confidence interval. The model was evaluated using the area under the curve (AUC) metric. Results: Overall, there were 205 patients included in the analysis. In the multivariate analysis, the most significant contributors to predicting technical success of ERCP were expert endoscopic experience and non-Roux-en Y anatomy. The elastic net model demonstrated moderate predictive performance, with an AUC of 0.656. Conclusions: The findings emphasize the importance of tailored procedural planning to optimize ERCP success in patients with altered anatomy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".