MétaCan
Menu
Back to cohort
Record W2576182667 · doi:10.3748/wjg.v23.i5.792

Sinusoid endotheliitis as a histological parameter for diagnosing acute liver allograft rejection

2017· article· en· W2576182667 on OpenAlexafffund
Yu Shi, Kun Dong, Yuguo Zhang, René P. Michel, Victoria Marcus, Yuyue Wang, Yu Chen, Zu‐Hua Gao

Bibliographic record

VenueWorld Journal of Gastroenterology · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsDr. Everett Chalmers Regional HospitalHorizon Health NetworkMcGill University
FundersMcGill University
KeywordsMedicinePredictive valueGastroenterologyPathologyPredictive value of testsHepatocyteInternal medicineUrologyBiologyIn vitro

Abstract

fetched live from OpenAlex

AIM: To investigated the feasibility of using sinusoidal endotheliitis (SE) as a histological marker for liver allograft rejection. METHODS: We compared the histological features of 88 liver allograft biopsies with acute cellular rejection (ACR) and 59 cases with no evidence of ACR. SE was scored as: (1) focal linear lifting up of the endothelial cells by lymphocytes with no obvious damage to adjacent hepatocytes; (2) focal disruption of the endothelial lining by a cluster of subendothelial lymphocytes (a group of > 3 lymphocytes); and (3) severe confluent endotheliitis with hemorrhage and adjacent hepatocyte loss. RESULTS: < 0.001) when correlating SE with the venous endotheliitis activity index only. CONCLUSION: Our data suggest that SE scoring could be a reliable and reproducible supplemental parameter to the existing Banff schema for diagnosing acute liver allograft rejection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.314
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueWorld Journal of GastroenterologySame topicOrgan Transplantation Techniques and OutcomesFrench-language works237,207