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Record W3049343087 · doi:10.1111/tri.13718

Impact of belatacept conversion on kidney transplant function, histology, and gene expression ‐ a single‐center study

2020· article· en· W3049343087 on OpenAlexaff
Gaurav Gupta, Marc Raynaud, Dhiren Kumar, Pooja Sanghi, Jessica Chang, Pam Kimball, Le Kang, Marlon F. Levy, Amit Sharma, Chandra Bhati, Layla Kamal, Idris Yakubu, H. Davis Massey, Chelsea Kidd, Anne L. King, Philip F. Halloran

Bibliographic record

VenueTransplant International · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsThe Metabolomics Innovation Centre
Fundersnot available
KeywordsMedicineBelataceptSingle CenterHistologyKidney transplantNephrologyInternal medicineKidney transplantationOncologyKidney

Abstract

fetched live from OpenAlex

) over 2-year follow-up. Patients converted < 6 months post-transplant were more likely to have a long-term improvement in kidney function. Paired gene expression analysis of 30 (of 53) consecutive pre- and postconversion surveillance biopsies did not reveal changes in inflammation/acute injury; although atrophy-fibrosis score worsened (mean = 0.28 to 0.44; P = 0.005). Thus, improvement in renal function with belatacept conversion occurred early and then sustained in comparison with controls where renal function remained unchanged overtime. We were unable to show molecular signals that could be related to CNI administration and regressed after withdrawal.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.572

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 head0.033
GPT teacher head0.296
Teacher spread0.263 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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