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Intraoperative dialysis during liver transplantation with citrate dialysate

2009· article· en· W2046767608 on OpenAlexvenueno aff
Robert M. Winrow, Connie L. Davis, Jeffrey B. Halldorson, Suhail Ahmad

Bibliographic record

VenueHemodialysis International · 2009
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDialysisFulminant hepatic failureHemodialysisLiver transplantationTransplantationFulminantSurgeryHeparinAnticoagulantAnesthesia

Abstract

fetched live from OpenAlex

Liver transplantation for acutely ill patients with fulminant liver failure carries high intraoperative and immediate postoperative risks. These are increased with the presence of concomitant acute kidney injury (AKI) and intraoperative dialysis is sometimes required to allow the transplant to proceed. The derangements in the procoagulant and anticoagulant pathways during fulminant liver failure can lead to difficulties with anticoagulation during dialysis, especially when continued in the operating room. Systemic anticoagulation is unsafe and regional citrate anticoagulation in the absence of a functional liver carries the risk of citrate toxicity. Citrate dialysate, a new dialysate with citric acid can be used for anticoagulation in patients who cannot tolerate heparin or regional citrate. We report a case of a 40-year-old female with acetaminophen-induced fulminant liver failure with associated AKI who underwent intraoperative dialytic support during liver transplantation anticoagulated with citrate dialysate during the entire procedure. The patient tolerated the procedure well without any signs of citrate toxicity and maintained adequate anticoagulation for patency of the dialysis circuit. Citrate dialysate is a safe alternative for intradialytic support of liver transplantation in fulminant liver failure.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.999

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.360
Teacher spread0.304 · 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.

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

Citations6
Published2009
Admission routes1
Has abstractyes

Explore more

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