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Record W2511033153 · doi:10.1111/ajt.14019

Donor BMI >30 Is Not a Contraindication for Live Liver Donation

2016· article· en· W2511033153 on OpenAlexaff
M. Knaak, Nicolás Goldaracena, Adam Doyle, Mark S. Cattral, Paul D. Greig, L. Lilly, Ian D. McGilvray, Gary Levy, Anand Ghanekar, Eberhard L. Renner, David Grant, Markus Selzner, Nazia Selzner

Bibliographic record

VenueAmerican Journal of Transplantation · 2016
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsToronto General Hospital
FundersAstellas PharmaAstellas Pharma US
KeywordsMedicineSteatosisContraindicationLiver transplantationInternal medicineBody mass indexFatty liverObesityTransplantationGastroenterologySurgeryDonationComplicationPathology

Abstract

fetched live from OpenAlex

The increased prevalence of obesity worldwide threatens the pool of living liver donors. Although the negative effects of graft steatosis on liver donation and transplantation are well known, the impact of obesity in the absence of hepatic steatosis on outcome of living donor liver transplantation (LDLT) is unknown. Consequently, we compared the outcome of LDLT using donors with BMI <30 versus donors with BMI ≥30. Between April 2000 and May 2014, 105 patients received a right-lobe liver graft from donors with BMI ≥30, whereas 364 recipients were transplanted with grafts from donors with BMI <30. Liver steatosis >10% was excluded in all donors with BMI >30 by imaging and liver biopsies. None of the donors had any other comorbidity. Donors with BMI <30 versus ≥30 had similar postoperative complication rates (Dindo-Clavien ≥3b: 2% vs. 3%; p = 0.71) and lengths of hospital stay (6 vs. 6 days; p = 0.13). Recipient graft function, assessed by posttransplant peak serum bilirubin and international normalized ratio was identical. Furthermore, no difference was observed in recipient complication rates (Dindo-Clavien ≥3b: 25% vs. 20%; p = 0.3) or lengths of hospital stay between groups. We concluded that donors with BMI ≥30, in the absence of graft steatosis, are not contraindicated for LDLT.

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 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 score0.664
Threshold uncertainty score0.359

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 integrity0.0000.000
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 head0.014
GPT teacher head0.281
Teacher spread0.267 · 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 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

Citations54
Published2016
Admission routes1
Has abstractyes

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