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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 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: Observational · 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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.056
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 source (direct Gemma or distilled Codex), 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

Citations6
Published2009
Admission routes1
Has abstractyes

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