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Sirolimus is not always responsible for new‐onset proteinuria after conversion for chronic allograft nephropathy

2007· article· en· W2007824826 on OpenAlexaff
Zainab Abdurrahman, Minnie Sarwal, Maria T. Millan, Susan J. Robertson, Guido Filler

Bibliographic record

VenuePediatric Transplantation · 2007
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineProteinuriaTacrolimusTransplantationImmunosuppressionSirolimusNephropathyUrologyKidney transplantationGastroenterologyChronic allograft nephropathyRenal biopsyInternal medicineImmunologyBiopsyKidneyEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

An eight-yr-old combined liver and kidney transplant recipient for hyperoxaluria type I developed significant proteinuria and hypertension after conversion of a Tacrolimus, MMF, and corticosteroids-based immunosuppression to Sirolimus, low-dose Tacrolimus, and corticosteroids six and a half yr after the transplant for chronic allograft nephropathy. There was only one class I HLA match and the recipient had multiple blood exposures prior to transplantation. The patient was treated with combined hemodialysis and peritoneal dialysis while awaiting transplantation to reduce the oxalate load. A renal biopsy revealed a de novo transplant glomerulopathy that was associated with specific HLA antibodies unrelated to the donor (HLA DR 17 and 18). After reintroduction of MMF, these antibodies became undetectable and the proteinuria completely resolved. We hypothesize that HLA antibodies may cause transplant glomerulopathy even if they are not donor-specific. Their production appears more susceptible to MMF therapy. A thorough work-up of new-onset proteinuria after conversion to Sirolimus should be performed, including an immunological work-up and a renal biopsy.

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 score0.165
Threshold uncertainty score0.932

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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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