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Record W4303644369 · doi:10.1002/ajmg.c.32006

Disease mechanisms of monogenic congenital anomalies of the kidney and urinary tract

2022· review· en· W4303644369 on OpenAlexafffund
Dervla M. Connaughton, Friedhelm Hildebrandt

Bibliographic record

VenueAmerican Journal of Medical Genetics Part C Seminars in Medical Genetics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesAcademic Medical Organization of Southwestern OntarioSchulich School of Medicine and DentistryDepartment of Medicine, Georgetown UniversityNational Institutes of HealthWestern University
KeywordsUrinary systemMendelian inheritanceKidney diseaseKidney developmentDiseaseKidneyPathogenesisEtiologyMedicineBiologyGeneticsPathologyInternal medicineGene

Abstract

fetched live from OpenAlex

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is a developmental disorder of the kidney and/or genito-urinary tract that results in end stage kidney disease (ESKD) in up to 50% of children. Despite the congenital nature of the disease, CAKUT accounts for almost 10% of adult onset ESKD. Multiple lines of evidence suggest that CAKUT is a Mendelian disorder, including the observation of familial clustering of CAKUT. Pathogenesis in CAKUT is embryonic in origin, with disturbances of kidney and urinary tract development resulting in a heterogeneous range of disease phenotypes. Despite polygenic and environmental factors being implicated, a significant proportion of CAKUT is monogenic in origin, with studies demonstrating single gene defects in 10%-20% of patients with CAKUT. Here, we review monogenic disease causation with emphasis on the etiological role of gene developmental pathways in CAKUT.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.299
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2022
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Medical Genetics Part C Seminars in Medical GeneticsSame topicRenal and related cancersFrench-language works237,207