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Record W4412039581 · doi:10.1111/ctr.70226

Chronic Antibiotic Prophylaxis to Prevent Recurrent Cholangitis in Liver Transplant Recipients

2025· article· en· W4412039581 on OpenAlexaff
Florence Runyo, Ahmad Almtrafi, Sagar Kothari, Trevor Reichman, Coleman Rotstein

Bibliographic record

VenueClinical Transplantation · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineLiver transplantationAntibiotic prophylaxisAntibioticsIntensive care medicineInternal medicineGastroenterologyTransplantationMicrobiology

Abstract

fetched live from OpenAlex

ABSTRACT Objective To evaluate the use of prolonged antibiotic prophylaxis (AP) on the prevention of recurrent biliary tract infection (BTI) in liver transplant recipients (LTR). Methods A single‐center retrospective case series study from 2009 to 2023 was conducted to assess the effect of AP on the prevention of recurrent BTI in LTR. Results A total of 15 patients who underwent liver transplantation (LT) were evaluated and treated with a prolonged course of AP (mean 77 months). Ten of fifteen (66.6%) patients experienced resolution of episodes of BTI while on AP. Three additional patents noted a marked decrease in the frequency of recurrent BTI. The AP was well tolerated with few adverse events. A rescue antibiotic strategy was provided for each patient on AP in case of breakthrough infection. Conclusion Prolonged AP appears to be an efficacious and safe method to prevent recurrent BTI in LTR, reducing admissions to the hospital. This approach requires more rigorous testing in a randomized clinical trial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.369
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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