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Record W2744493768 · doi:10.5604/01.3001.0010.2809

Clinical Characteristics and Complications of Pediatric Liver Biopsy: A Single Centre Experience

2017· article· en· W2744493768 on OpenAlexaff
Patricia Almeida, Richard A. Schreiber, Jennifer Liang, Quais Mujawar, Orlee R. Guttman

Bibliographic record

VenueAnnals of Hepatology · 2017
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaBC Children's Hospital
Fundersnot available
KeywordsMedicineLiver biopsyGold standard (test)BiopsyComplicationCholestasisPercutaneousMedical recordSurgeryLiver diseaseRetrospective cohort studyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Percutaneous liver biopsy (LB) is the gold standard method for evaluation and management of patients with liver disease. The purpose of this study was to characterize pediatric patients undergoing LB at British Columbia Children's Hospital, and to determine the rate and timing of complications following the procedure. MATERIAL AND METHODS: The medical records of all pediatric patients who underwent LB during a six-year retrospective study were reviewed to collect demographic and procedure-related data. RESULTS: 223 LBs were performed, and 179 of these biopsies were percutaneous or transjugular. Elevated liver enzymes and cholestasis together accounted for almost 70% of the indications for LB, and the histological analysis of liver tissue yielded a specific diagnosis in 89 % of the cases. There were no deaths and no major complications related to LB. The most frequent minor complication was pain (59% of LBs) and the other complications were bleeding-related and classified as minor. The vast majority of complications (88%) were recognized within 8 h of the LB. CONCLUSIONS: LB is a valuable and safe procedure in pediatric patients with a low rate of complications. Pediatric patients can be discharged home safely should no complications occur within the first 8-12 h after the procedure.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.173
GPT teacher head0.413
Teacher spread0.239 · 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 teacher head, 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

Citations28
Published2017
Admission routes1
Has abstractyes

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