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Record W4323668492 · doi:10.1016/j.hpb.2023.03.006

Expanding the living donor pool using domino liver transplantation: a systematic review

2023· review· en· W4323668492 on OpenAlexaboutno aff
Mika S. Buijk, Jop B L van der Meer, Jan N.M. IJzermans, Robert C. Minnee, Markus Boehnert

Bibliographic record

VenueHPB · 2023
Typereview
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLiver transplantationEconomic shortageLiver diseaseTransplantationSurgeryPortal veinMortality rateLive donorDominoLiving donor liver transplantationInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: To this day, a discrepancy exists between donor liver demand and supply. Domino liver transplantation (DLT) can contribute to increasing the number of donor livers available for transplantation. METHODS: The design of this systematic review was based on the Preferred Reporting Items for Systematic Reviews (PRISMA). A qualitative analysis of included studies was performed. Primary outcomes were mortality and peri- and postoperative complications related to DLT. RESULTS: Twelve studies met the inclusion criteria. All included studies showed that DLT outcomes were comparable to outcomes of deceased donor liver transplantation (DDLT) in terms of mortality and complications. One-year patient survival rate ranged from 66.7% to 100%. Re-transplantation rate varied from 0 to 12.5%. Most frequent complications were related to biliary (3.7%-37.5%), hepatic artery (1.6%-9.1%), portal vein (12.5-33.3%) and hepatic vein events (1.6%), recurrence of domino donor disease (3.3%-17.4%) and graft rejection (16.7%-37.7%). The quality of the evidence was rated as moderate according to the Newcastle-Ottawa scale (NOS). CONCLUSION: DLT outcomes were similar to DDLT in terms of mortality and complications. Even though DLT will not solve the entire problem of organ shortage, transplant programs should always consider using this tool to maximize the availability of liver grafts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.030
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.093
GPT teacher head0.378
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations30
Published2023
Admission routes1
Has abstractyes

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