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Record W2765464384 · doi:10.1111/petr.13076

Kidney transplantation in a child with Pierson syndrome

2017· article· en· W2765464384 on OpenAlexaff
Sanem Güler, Phillip Acott, Kathy Whelan, Michele Molinari

Bibliographic record

VenuePediatric Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsJaneway Children's Health and Rehabilitation CentreIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsMedicineNephrotic syndromeCongenital nephrotic syndromeTransplantationKidneyKidney transplantationSlit diaphragmRenal functionPediatricsInternal medicinePodocyteProteinuria

Abstract

fetched live from OpenAlex

Congenital nephrotic syndrome is commonly associated with mutations in genes that encode podocyte and slit diaphragm proteins or the structural and regulatory proteins of the GBM. These mutations lead to the formation of dysfunctional proteins, which account for the resistance of the renal manifestations to conventional treatment methods. Consequently, patients become renal replacement therapy dependent. Mutation of the LAMB2 gene is associated with Pierson syndrome, which is an autosomal recessive disorder characterized by congenital nephrotic syndrome and ocular abnormalities. In this report, a 2-year-old male patient who was diagnosed with Pierson syndrome is presented. He had bilateral microcoria and developmental delay in addition to nephrotic syndrome. His renal function deteriorated rapidly, and he underwent a deceased donor kidney transplantation. He showed dramatic improvement after kidney transplantation; in addition to having good renal function, he started to catch up to his peers in terms of growth. Pierson syndrome should be considered during the diagnostic investigations of children with renal manifestations and ocular abnormalities. Children with Pierson syndrome must be evaluated in terms of kidney transplantation as soon as they are diagnosed.

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 score0.015
Threshold uncertainty score0.515

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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