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Record W3002105790 · doi:10.2215/cjn.08970719

Recurrence of FSGS after Kidney Transplantation in Adults

2020· article· en· W3002105790 on OpenAlexfundno aff
Audrey Uffing, María José Pérez‐Sáez, Marilda Mazzali, Roberto Ceratti Manfro, Andréa Carla Bauer, Frederico de Sottomaior Drumond, Michelle M. O’Shaughnessy, Xingxing S. Cheng, Kuo‐Kai Chin, Carlucci Gualberto Ventura, Fabiana Agena, Elias David‐Neto, Juliana Mansur, Gianna Mastroianni Kirsztajn, Hélio Tedesco‐Silva, Gilberto M.V. Neto, Carlos Arias-Cabrales, Anna Buxeda, Mathilde Bugnazet, Thomas Jouvé, Paolo Malvezzi, Enver Akalin, Omar Alani, Nikhil Agrawal, Gaetano La Manna, Giorgia Comai, Claudia Bini, Saif A. Muhsin, Miguel C. Riella, Sílvia Regina Hokazono, Samira Farouk, Meredith Haverly, Suraj Sarvode Mothi, Stefan P. Berger, Paolo Cravedi, Leonardo V. Riella

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesMallinckrodt PharmaceuticalsNephcure FoundationHarvard UniversityHarvard CatalystPfizerBristol-Myers SquibbAmerican Heart Association
KeywordsMedicineHazard ratioTransplantationInterquartile rangeInternal medicineKidney transplantationKidney diseaseProportional hazards modelCohort studyCohortIncidence (geometry)Confidence intervalRisk factorSurgery

Abstract

fetched live from OpenAlex

Background and objectives FSGS recurrence after kidney transplantation is a major risk factor for graft loss. However, the natural history, clinical predictors, and response to treatment remain unclear because of small sample sizes and poor generalizability of single-center studies, and disease misclassification in registry-based studies. We therefore aimed to determine the incidence, predictors, and treatment response of recurrent FSGS in a large cohort of kidney transplant recipients. Design, setting, participants, & measurements The Post-Transplant Glomerular Disease (TANGO) project is an observational, multicenter, international cohort study that aims to investigate glomerular disease recurrence post-transplantation. Transplant recipients were screened for the diagnosis of idiopathic FSGS between 2005 and 2015 and details were recorded about the transplant, clinical outcomes, treatments, and other risk factors. Results Among 11,742 kidney transplant recipients screened for FSGS, 176 had a diagnosis of idiopathic FSGS and were included. FSGS recurred in 57 patients (32%; 95% confidence interval [95% CI], 25% to 39%) and 39% of them lost their graft over a median of 5 (interquartile range, 3.0–8.1) years. Multivariable Cox regression revealed a higher risk for recurrence with older age at native kidney disease onset (hazard ratio [HR], 1.37 per decade; 95% CI, 1.09 to 1.56). Other predictors were white race (HR, 2.14; 95% CI, 1.08 to 4.22), body mass index at transplant (HR, 0.89 per kg/m 2 ; 95% CI, 0.83 to 0.95), and native kidney nephrectomies (HR, 2.76; 95% CI, 1.16 to 6.57). Plasmapheresis and rituximab were the most frequent treatments (81%). Partial or complete remission occurred in 57% of patients and was associated with better graft survival. Conclusions Idiopathic FSGS recurs post-transplant in one third of cases and is associated with a five-fold higher risk of graft loss. Response to treatment is associated with significantly better outcomes but is achieved in only half of the cases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.360
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), 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".

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Citations193
Published2020
Admission routes1
Has abstractyes

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