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Record W2953282967 · doi:10.1093/ndt/gfz101.sao014

SaO014ALLOGRAFT GENE EXPRESSION ASSESSMENT PREDICTS GRAFT LOSS IN KIDNEY RECIPIENTS WITH CHRONIC ANTIBODY-MEDIATED REJECTION

2019· article· en· W2953282967 on OpenAlexaff
Denis Viglietti, Benjamin Adam, Jean–Paul Duong Van Huyen, Olivier Aubert, Denis Glotz, Christophe Legendre, Alexandre Loupy, Carmen Lefaucheur, Michael Mengel

Bibliographic record

VenueNephrology Dialysis Transplantation · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineGene expressionKidney diseaseKidneyKidney transplantationAntibodyGeneImmunologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

INTRODUCTION: Chronic antibody-mediated rejection (cAMR) is a major contributor to kidney allograft loss. However, prognostic heterogeneity has not been addressed. We investigated whether mechanistically-informed prediction of allograft loss could identify patients with different outcomes. METHODS: We prospectively enrolled all adult kidney recipients from two centers at the time of the first post-transplant allograft biopsy showing cAMR according to Banff criteria (2005-2016). All patients were assessed for cAMR phenotype based on histology, immunochemistry and gene expression using Nanostring technology on formalin-fixed paraffin-embedded tissue for 209 pathogenesis-selected genes previously identified for their relevance to transplant diagnosis. All patients underwent anti-HLA antibody, GFR and proteinuria evaluation at the time of cAMR diagnosis, and were followed up to September 2018. RESULTS: Among 151 patients meeting inclusion criteria, cAMR was diagnosed at a median time of 23 (IQR, 10-58) months after transplantation Cross-validated supervised principal component analysis identified two independent linear combinations of 20 genes associated with allograft loss, which were mainly related to B cell immunity and immunoglobulin-mediated immune response. We built a prognostic gene score based on the two components that exhibited good optimism-corrected calibration and greater time-dependent accuracy (5-year AUC of 0.80) than clinical and histological parameters for predicting allograft loss (5-year AUC for GFR, proteinuria and cg score: 0.67, 0.70 and 0.65, respectively). The prognostic gene score identified three groups of patients: low-score patients (n=22) with a 5-year allograft survival of 95%, intermediate-score patients (n=65) with a 5-year allograft survival of 64% and high-score patients (n=64) with a 5-year graft survival of 29% (p<0.001). In multivariable analysis, the prognostic gene score predicted graft loss independently of clinical, immunological and histological parameters (adjusted HR: 1.70, 95%CI: 1.22-2.36, p=0.002). CONCLUSIONS: By addressing prognostic heterogeneity in cAMR at the molecular level, we defined a pathogenesis-based prognostic score that outperformed clinical, immunological and histological parameters for predicting kidney allograft loss.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.004
GPT teacher head0.239
Teacher spread0.236 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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