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Living-Donor Right Hepatectomy with or without Inclusion of Middle Hepatic Vein: Comparison of Morbidity and Outcome in 56 Patients

2004· article· en· W2007645358 on OpenAlexaff
Mark S. Cattral, Michele Molinari, Charles M. Vollmer, Ian D. McGilvray, Alice C. Wei, Mark Walsh, Lesley Adcock, Nikki J. Marks, Les Lilly, Nigel Girgrah, Gary Levy, Paul D. Greig, David Grant

Bibliographic record

VenueAmerican Journal of Transplantation · 2004
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineSurgeryHepatectomyVeinProspective cohort study

Abstract

fetched live from OpenAlex

Venous congestion of segments V and VIII is observed frequently in living-donor right lobe liver transplants without middle hepatic vein (MHV) drainage, and can be a cause of graft dysfunction and failure. Inclusion of the MHV with the graft is controversial, however, because of the perceived potential for increased donor morbidity. We compared the outcome of living liver donors in whom the MHV was either left intact in the donor (group 1; n = 28) or was removed with the graft (group 2; n = 28). All prospective donors completed an extensive multidisciplinary evaluation to determine suitability for surgery and to ensure that the MHV could be removed safely without compromising venous outflow from the remaining liver. Patient demographics including age, weight, body-mass index, and liver volumetry as determined by computerized tomography were similar in both groups. Operative time in group 2 was significantly shorter than in group 1. There was no difference in estimated blood loss, transfusion requirements, peak serum liver tests, time interval from surgery to complete normalization of liver tests, complications, and length of hospitalization. We conclude that including the MHV with living-donor right lobe grafts can be performed safely in most donors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.303
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 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

Citations83
Published2004
Admission routes1
Has abstractyes

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