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Record W2088783838 · doi:10.1111/ctr.12170

Successful conversion to belatacept after thrombotic microangiopathy in kidney transplant patients

2013· article· en· W2088783838 on OpenAlexaff
Sireesha Koppula, Sarah E. Yost, Amy N. Sussman, Erika Bracamonte, Bruce Kaplan

Bibliographic record

VenueClinical Transplantation · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Alberta
Fundersnot available
KeywordsThrombotic microangiopathyBelataceptMedicineImmunosuppressionMicroangiopathyKidney transplantationComplicationTransplantationImmunologyInternal medicineKidney transplantDiseaseDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

Thrombotic microangiopathy (TMA) is a severe complication of kidney transplantation. TMA may occur de novo or as recurrent disease post-transplant. De novo disease is usually associated with immunosuppressive drugs or can be seen as a part of endothelial damage that accompanies antibody-mediated rejection. Treatment for de novo TMA is limited to plasma exchange and change in immunosuppression. We report two cases of de novo TMA post-transplant that were successfully treated by converting to belatacept for maintenance immunosuppression.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.998

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)0.0030.004

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 head0.017
GPT teacher head0.288
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

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

Citations25
Published2013
Admission routes1
Has abstractyes

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