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Improved outcome with immunosuppressive monotherapy after renal transplantation in Schimke‐immuno‐osseous dysplasia

2008· article· en· W2006107564 on OpenAlexafffund
Thomas Lücke, Nele Kanzelmeyer, Alireza Baradaran‐Heravi, Cornelius F. Boerkoel, Michael Burg, J. H. H. Ehrich, Lars Pape

Bibliographic record

VenuePediatric Transplantation · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
FundersMedizinischen Hochschule HannoverUniversity of British ColumbiaEuropean Synchrotron Radiation Facility
KeywordsMedicineDysplasiaTransplantationInternal medicine

Abstract

fetched live from OpenAlex

SIOD is a multisystem disorder caused by a mutant chromatin remodelling protein. The main clinical findings are spondyloepiphyseal dysplasia with disproportionate growth restriction, defective cellular immunity, and steroid-resistant nephrotic syndrome secondary to biopsy proven FSGS leading to ESRF. Concerning ESRF, kidney transplantation is the therapy of choice since FSGS does not recur in the graft. However, with respect to the underlying immune disorder and the increased susceptibility to life threatening infections, the question of the optimal immunosuppressive therapy after renal transplantation remains unresolved. Under conventional immunosuppressive regimens some SIOD patients have developed severe disseminated cutaneous papilloma virus infections or EBV associated lymphoproliferative disease. We present several cases of children with SIOD (four of five had SMARCAL1 mutations) and monotherapy maintenance immunosuppression after renal transplantation and compare them with 13 patients from the SIOD registry. We have found that post-renal transplantation immunosuppressive monotherapy results in a good outcome with a reduced number of severe infections. Due to the underlying immunodeficiency in SIOD, limited immunosuppression may be possible without increasing the risk of acute or chronic rejection.

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 score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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

Citations20
Published2008
Admission routes2
Has abstractyes

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