Cholangitis Definition and Treatment after Kasai Hepatoportoenterostomy for Biliary Atresia: A Delphi Process and International Expert Panel
Bibliographic record
Abstract
(1) Background: Acute cholangitis during the first year after Kasai hepatoportoenterostomy (HPE) has a negative impact on patient and native liver survival. There are no consistent guidelines for the definition, treatment, and prophylaxis of cholangitis after HPE. The aim of this study was to develop definition, treatment, and prophylaxis guidelines to allow for expeditious management and for standardization in reporting. (2) Methods: the Delphi method, an extensive literature review, iterative rounds of surveys, and expert panel discussions were used to establish definition, treatment, and prophylaxis guidelines for cholangitis in the first year after HPE. (3) Results: Eight elements (pooled into two groups: clinical and laboratory/imaging) were identified to define cholangitis after HPE. The final proposed definitions for suspected and confirmed cholangitis are a combination of one element, respectively, two elements from each group; furthermore, the finding of a positive blood culture was added to the definition of confirmed cholangitis. The durations for prophylaxis and treatment of suspected and confirmed cholangitis were uniformly agreed upon by the experts. (4) Conclusions: for the first time, an international consensus was found for guidelines for definition, treatment, and prophylaxis for cholangitis during the first year after Kasai HPE. Applicability will need further prospective multicentered studies.
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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 | 0.240 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".