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Record W1920140916 · doi:10.1111/petr.12556

Donor‐specific anti‐<scp>HLA</scp> antibodies in pediatric renal transplant recipients with creeping creatinine: Prevalence, histological correlations, and impact on patient and graft survival

2015· article· en· W1920140916 on OpenAlexaff
Marta Lidia Monteverde, Alicia Chaparro, J. Goldberg, C.Y. Marcos, K. Padros, Ziomara Balbarrey, Liliana Briones, David N. Rush

Bibliographic record

VenuePediatric Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineBiopsyHistopathologyGastroenterologyCreatinineUrologyFibrosisInternal medicineAntibodyHuman leukocyte antigenPathologyImmunologyAntigen

Abstract

fetched live from OpenAlex

Donor-specific anti-HLA antibodies (DSA) causing CAMR are responsible for a high proportion of long-term graft failures after RTX. We studied the prevalence of DSA in RTX children biopsied for creeping Cr, its relationship with NA, and patient and graft survival according to histopathology. Between 2008 and 2013, 92 children were biopsied at a median of 38 months post-RTX. At biopsy, the prevalence of DSA was 49% and C4d 70%. NA rate was 45%, higher in adolescents (60%). Most frequent diagnoses were CAMR (72%) and interstitial fibrosis with tubular atrophy (IFTA) (28%). Forty-five of 66 patients with CAMR (68%) had detectable DSA. Twenty-one DSA-negative patients with CAMR had histological damage (IFTA + C4d positivity). C4d was detected in 64 of 66 biopsies with CAMR. Recipients with IFTA alone had neither C4d, nor detectable DSA, and were adherent. Graft survival at five yr was 89% in patients with CAMR, 79% in those with CAMR + TCMR Banff I, 33% in those with CAMR + TCMR Banff II, and 96% in those with IFTA. ABMR and complement activation were frequent in children biopsied for creeping Cr. Recipients with DSA were more likely to be non-adherent and have CAMR or CAMR + TCMR and worse graft survival.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.025
GPT teacher head0.273
Teacher spread0.248 · 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.

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

Citations8
Published2015
Admission routes1
Has abstractyes

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