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Focal segmental glomerular sclerosis in renal transplant recipients: predicting early disease recurrence may prolong allograft function

2009· article· en· W2099324608 on OpenAlexaff
Alp Şener, Anthony J Bella, Chris Nguan, Patrick Luke, Andrew A. House

Bibliographic record

VenueClinical Transplantation · 2009
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of OttawaWestern University
Fundersnot available
KeywordsMedicineTransplantationPlasmapheresisRenal functionDialysisNephrectomyUrologySurgeryFocal segmental glomerulosclerosisInternal medicineKidney transplantationGlomerulosclerosisProteinuriaGastroenterologyKidneyImmunologyAntibody

Abstract

fetched live from OpenAlex

Recurrence of focal segmental glomerular sclerosis (FSGS) in the allograft following renal transplantation can be graft threatening. To assess risk factors associated with FSGS recurrence, we analyzed 22 patients with FSGS who underwent transplantation between 1996 and 2004. Five patients (Group I, 23%) developed FSGS post-transplantation. Of these patients, 60% had undergone bilateral nephrectomy (BN) for progressive disease compared with none of the patients that were free of recurrence (Group II) (p = 0.0006). Other factors linked with recurrent FSGS were time to first dialysis (Group I: 3.1 +/- 1.1 yr vs. Group II: 11.9 +/- 1.9 yr; p = 0.03), pre-transplant proteinuria (Group I: 7.0 +/- 1.8 g/d vs. Group II: 2.5 +/- 0.7 g/d; p = 0.02), young age at transplantation (p = 0.09) and female sex (Group I: 80% vs. Group II: 24%; p = 0.021). Eighty percent of Group I patients received a living related transplant vs. 24% in Group II (p = 0.021). All grafts continue to function at last follow-up with comparable serum creatinines. Overall, post-transplant FSGS recurrence may be associated with BN, severity of pre-transplant FSGS, female gender, and living donation. These patients should be monitored closely for early recurrence and may benefit from early plasmapheresis to restore and facilitate long-term graft function.

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)0.001
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 score0.055
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.325
Teacher spread0.282 · 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

Citations54
Published2009
Admission routes1
Has abstractyes

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