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Record W2919298451 · doi:10.1097/tp.0000000000002682

Subclinical Inflammation in Renal Transplantation

2019· review· en· W2919298451 on OpenAlexaffabout
David N. Rush, Ian W. Gibson

Bibliographic record

VenueTransplantation · 2019
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineSubclinical infectionImmunosuppressionTransplantationInflammationImmunologyInternal medicinePathology

Abstract

fetched live from OpenAlex

The standardization of renal allograft pathology began in 1991 at the first Banff Conference held in Banff, Alberta, Canada. The first task of transplant pathologists, clinicians, and surgeons was to establish diagnostic criteria for T-cell-mediated rejection (TCMR). The histological threshold for this diagnosis was arbitrarily set at "i2t2": a mononuclear interstitial cell infiltrate present in at least 25% of normal parenchyma and >4 mononuclear cells within the tubular basement membrane of nonatrophic tubules. TCMR was usually found in dysfunctional grafts with an elevation in the serum creatinine; however, our group and others found this extent of inflammation in "routine" or "protocol" biopsies of normally functioning grafts: "subclinical" TCMR. The prevalence of TCMR is higher in the early months posttransplant and has decreased with the increased potency of current immunosuppressive agents. However, the pathogenicity of lesser degrees of inflammation under modern immunosuppression and the relation between ongoing inflammation and development of donor-specific antibody has renewed our interest in subclinical alloreactivity. Finally, the advances in our understanding of pretransplant risk assessment, and our increasing ability to monitor patients less invasively posttransplant, promises to usher in the era of precision medicine.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.102
GPT teacher head0.407
Teacher spread0.305 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations36
Published2019
Admission routes2
Has abstractyes

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