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Record W4225097807 · doi:10.1111/ctr.14690

Perioperative management of living donor liver transplantation: Part 2 – Donors

2022· review· en· W4225097807 on OpenAlexaff
Tetsuro Sakai, Justin Sangwook Ko, Cara Crouch, Sathish S. Kumar, Gyu‐Seong Choi, Florian Hackl, Dai Hoon Han, Michael D. Kaufman, Seong Hoon Kim, Carla Luzzi, Stuart A. McCluskey, Won Jung Shin, Joel Sirianni, Ki Won Song, Cinnamon Sullivan, Adrian Hendrickse

Bibliographic record

VenueClinical Transplantation · 2022
Typereview
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of Toronto
FundersUniversity of Pittsburgh
KeywordsMedicinePerioperativeDonationLiver transplantationEconomic shortageTransplantationSurgeryHepatectomyLiver diseaseOrgan donationGeneral surgeryIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Living donor liver transplantation was first developed to mitigate the limited access to deceased donor organs in Asia in the 1990s. This alternative liver transplantation method has become a widely practiced and established transplantation option for adult patients suffering with end-stage liver disease, and it has successfully helped address the shortage of deceased donors. The Society for the Advancement of Transplant Anesthesia and the Korean Society of Transplantation Anesthesiologists jointly reviewed published studies on the perioperative management of adult live liver donors undergoing donor hemi-hepatectomy. The goal of the review is to offer transplant anesthesiologists and critical care physicians a comprehensive overview of the perioperative management of adult live donors. We featured the current status, donor selection process, outcomes and complications, surgical procedure, anesthetic management, Enhanced Recovery After Surgery protocols, avoidance of blood transfusion, and considerations for emergency donation. Recent surgical advances, including laparoscopic donor hemi-hepatectomy and robotic laparoscopic donor surgery, are also addressed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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)0.0040.000

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 head0.143
GPT teacher head0.426
Teacher spread0.282 · 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 designNot applicable
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

Citations21
Published2022
Admission routes1
Has abstractyes

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