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Record W2325651095 · doi:10.1177/1531003512454580

Hepatic Artery Transection Reconstructed With Splenic Artery Transposition Graft

2012· review· en· W2325651095 on OpenAlexaff
J. Faulds, Amanda Johner, Darren Klass, Andrezj K. Buczkowski, Charles H. Scudamore

Bibliographic record

VenuePerspectives in Vascular Surgery · 2012
Typereview
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineSplenic arteryArteryLigationCommon hepatic arteryTransposition (logic)SurgeryCardiologyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Hepatic artery transection presents a technical challenge in vascular reconstruction. Formal arterial repair is indicated in patients with underlying liver disease and those undergoing bile duct reconstructions because of a higher risk of complication following hepatic artery injury. This report highlights a novel approach to hepatic artery transection with splenic artery transposition. METHODS: A case of hepatic artery transection repaired with splenic artery transposition is presented with an accompanying literature review. RESULTS: During elective pancreaticoduodenectomy, the common hepatic artery was injured at its origin. The splenic artery was divided and transposed to the hepatic artery, thus restoring arterial flow to the liver and bile duct. CONCLUSION: Various strategies to manage a hepatic artery injury have been described, ranging from ligation to complex vascular reconstruction. In hemodynamically stable patients, arterial transposition using the splenic artery is a feasible method to ensure adequate arterial supply to the liver and biliary tract.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.295
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2012
Admission routes1
Has abstractyes

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