MétaCan
Menu
Back to cohort
Record W2751174878 · doi:10.1097/sla.0000000000002477

Defining Benchmarks in Liver Transplantation

2017· article· en· W2751174878 on OpenAlex
Xavier Muller, Francesca Marcon, Gonzalo Sapisochín, Max Marquez, Fédérica Dondero, Michel Rayar, Majella M.B. Doyle, Lauren Callans, Jun Li, Greg Nowak, Marc-Antoine Allard, Ina Jochmans, Kyle Jacskon, M. Chahdi Beltrame, Marjolein van Reeven, Samuele Iesari, Alessandro Cucchetti, Hemant Sharma, Roxane D. Staiger, Dimitri Aristotle Raptis, Henrik Petrowsky, Michelle de Oliveira, Roberto Hernandez‐Alejandro, Antonio D. Pinna, Jan Lerut, Wojciech G. Polak, Eduardo de Santibáñes, Martín de Santibañes, Andrew M. Cameron, Jacques Pirenne, Daniel Cherqui, René Adam, Bo-Göran Ericzon, Kim M. Olthoff, A. Shaked, William C. Chapman, Karim Boudjéma, Olivier Soubrane, Cathérine Paugam‐Burtz, Paul D. Greig, David Grant, A. Carvalheiro, Paolo Muiesan, Philipp Dutkowski, Milo A. Puhan, Pierre-Alain Clavien

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsWestern UniversityToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineLiver transplantationTransplantationSurgeryComplicationLiver diseasePercentileSingle CenterInternal medicine

Abstract

fetched live from OpenAlex

: This multicentric study of 17 high-volume centers presents 12 benchmark values for liver transplantation. Those values, mostly targeting markers of morbidity, were gathered from 2024 "low risk" cases, and may serve as reference to assess outcome of single or any groups of patients. OBJECTIVE: To propose benchmark outcome values in liver transplantation, serving as reference for assessing individual patients or any other patient groups. BACKGROUND: Best achievable results in liver transplantation, that is, benchmarks, are unknown. Consequently, outcome comparisons within or across centers over time remain speculative. METHODS: Out of 7492 liver transplantation performed in 17 international centers from 3 continents, we identified 2024 low risk adult cases with a laboratory model for end-stage liver disease score ≤20 points, a balance of risk score ≤9, and receiving a primary graft by donation after brain death. We chose clinically relevant endpoints covering intra- and postoperative course, with a focus on complications graded by severity including the complication comprehensive index (CCI). Respective benchmarks were derived from the median value in each center, and the 75 percentile was considered the benchmark cutoff. RESULTS: Benchmark cases represented 8% to 49% of cases per center. One-year patient-survival was 91.6% with 3.5% retransplantations. Eighty-two percent of patients developed at least 1 complication during 1-year follow-up. Biliary complications occurred in one-fifth of the patients up to 6 months after surgery. Benchmark cutoffs were ≤4 days for ICU stay, ≤18 days for hospital stay, ≤59% for patients with severe complications (≥ Grade III) and ≤42.1 for 1-year CCI. Comparisons with the next higher risk group (model for end stage liver disease 21-30) disclosed an increase in morbidity but within benchmark cutoffs for most, but not all indicators, while in patients receiving a second graft from 1 center (n = 50) outcome values were all outside of benchmark values. CONCLUSIONS: Despite excellent 1-year survival, morbidity in benchmark cases remains high with half of patients developing severe complications during 1-year follow-up. Benchmark cutoffs targeting morbidity parameters offer a valid tool to assess higher risk groups.

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.

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 score0.070
Threshold uncertainty score0.230

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 head0.160
GPT teacher head0.371
Teacher spread0.211 · 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