Prophylactic Antibiotics to Prevent Cholangitis in Children with Biliary Atresia After Kasai Portoenterostomy: A Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: A connection between the bowel and bile ducts after the Kasai hepatoportoenterostomy (HPE) procedure poses a risk of ascending cholangitis. There were only a few evidence-based consensuses on the benefits of prophylactic antibiotics. This study aims to assess the value of prophylactic antibiotics in reducing the risk of cholangitis following the Kasai HPE procedure. METHODS: Meta-analysis is performed using random-effects model from the search result of 5 online databases (PubMed, Google Scholar, EBSCO MEDLINE, ClinicalTrials.gov , and EuropePMC) from inception to October 27, 2021. The keywords used were "antibiotic," "antimicrobial," "Kasai," "portoenterostomy," "biliary atresia," and "bile duct atresia." Cochrane Risk of Bias tool and Newcastle-Ottawa Scale is used to assess the risk of bias. The outcomes are incidence of cholangitis and native liver survival. RESULTS: Six studies consisting of 4 cohorts and 2 cross-sectional studies were extracted. A total of 714 patients reported different cholangitis incidence after prophylactic antibiotics administration post-Kasai HPE. The incidence of cholangitis following Kasai HPE was not statistically significant among participants. There is conflicting evidence on the efficacy of antibiotics in prolonging native liver survival. CONCLUSIONS: The existing evidence does not support the administration of prophylactic antibiotics in preventing cholangitis after Kasai HPE among biliary atresia patients. Additionally, their roles in native liver survival are still inconclusive. The fact that there were heterogeneous method and antibiotic usage between existing studies must also be highlighted for better design in future 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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".