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Record W2529451483 · doi:10.1097/txd.0000000000000558

Conservative Pancreas Graft Preservation at the Extreme

2015· article· en· W2529451483 on OpenAlexaff
Jerome Laurence, Gonzalo Sapisochín, Markus Selzner, Andrea Norgate, Deepali Kumar, I. Mcgilvary, Paul D. Preig, Jeffrey Schiff, Mark S. Cattral

Bibliographic record

VenueTransplantation Direct · 2015
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsNature Conservancy of CanadaToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineSurgeryPancreasThrombosisPancreas transplantationPercutaneousPancreatectomyTransplantationKidney transplantationInternal medicineResection

Abstract

fetched live from OpenAlex

Because of the value some patients place in remaining insulin-independent after pancreas transplantation, they may be reluctant to undergo graft pancreatectomy, even in the face of extreme complications, such as graft thrombosis and duodenal segment leak. Partly, for this reason, a variety of complex salvage techniques have been described to save the graft in such circumstances. We report a case of a series of extreme complications related to a leak from the duodenal segment after a simultaneous pancreas and kidney transplant. These included infected thrombosis of the inferior vena cava associated with a graft venous thrombosis and a retroperitoneal fistula. The patient retained graft function with insulin independence and repeatedly declined graft pancreatectomy against the advice of the transplant team. Conservative treatment with percutaneous drainage, antibiotics, and anticoagulation was eventually successful. This outcome is unique in our experience and may be instructive to teams caring for pancreas transplant recipients.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.080
GPT teacher head0.303
Teacher spread0.223 · 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 designBench or experimental
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

Citations4
Published2015
Admission routes1
Has abstractyes

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