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Record W4392245103 · doi:10.14740/gr1687

Primary Non-Function of Hepatic Allograft With Preexisting Microvesicular Steatosis/Foamy Degeneration and Mild Large-Droplet Macrovesicular Steatosis

2024· article· en· W4392245103 on OpenAlexvenueno aff
Melissa E. Limia, Xiu Li Liu, Jennifer S. Yu, Kathleen Byrnes

Bibliographic record

VenueGastroenterology Research · 2024
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSteatosisMedicinePathologyCirrhosisFatty liverLiver transplantationTransplantationBiopsyGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

It has been established that more than mild large-droplet macrovesicular steatosis (LD-MAS) is associated with increased risk of graft non-function. In contrast, even severe small-droplet macrovesicular steatosis (SD-MAS) has been found to be less prognostically significant. It remains unclear if a donor liver with diffuse microvesicular steatosis is associated with an increased risk of graft dysfunction. A 56-year-old male with alcoholic cirrhosis was transplanted with a liver from a 42-year-old overweight male donor after brain death. The frozen section of the donor liver biopsy taken at harvest showed diffusely enlarged clear/foamy hepatocytes and mild LD-MAS (about 5-10% of total tissue). The reperfusion liver biopsy taken at time 0 of transplantation showed hemorrhage, pale and enlarged hepatocytes, and mild LD-MAS (about 10% of total tissue) with lipopeliosis. The graft became non-functional, and the patient was re-transplanted 24 h after the initial transplantation. Histologic examination of the failed liver allograft showed extensive hemorrhagic necrosis, neutrophilic inflammation, diffuse microvesicular steatosis, and large extracellular fat droplets (about 20% of total tissue). This case demonstrates that precautions are needed to avoid using livers with diffuse and severe microvesicular steatosis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.299
Teacher spread0.278 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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