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Record W1988517178 · doi:10.1007/s00147-004-0727-2

Hepatic abscesses after liver transplantation secondary to traumatic intrahepatic bile duct injuries in a cadaveric allograft donor

2004· article· en· W1988517178 on OpenAlexaff
Stuart Cowie, Eric M. Yoshida, Anthony G. Ryan, Stephen W. Chung, Andrezj K. Buczkowski, Stephen Ho, S. Erb, Urs P. Steinbrecher, Charles H. Scudamore

Bibliographic record

VenueTransplant International · 2004
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineLiver transplantationCholangiographyBile ductSurgeryIntrahepatic bile ductsBluntCadaveric spasmTransplantationLeft Hepatic DuctGeneral surgery

Abstract

fetched live from OpenAlex

We report the case of an ultimately successful liver transplant recipient whose post-transplant course was complicated by the early development of multiple abscesses in the graft. Post-transplant cholangiography identified multiple shear injuries to the second and third order intrahepatic bile ducts, originating from blunt trauma to the donor liver. Treatment was non-operative following recent reports of the successful management of intrahepatic bile duct injury in the stable trauma patient. This discussion adds to the limited literature available on the transplantation of injured donor livers, despite this being a relatively common practice. Further experience is needed in determining the appropriate criteria for the use of traumatized donor livers. Cholangiography carried out on the back table may help to determine if such injured livers are suitable for transplantation.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.264
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 designCase report
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

Citations7
Published2004
Admission routes1
Has abstractyes

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