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Record W3011486391 · doi:10.2215/cjn.12581019

A Primer on Congenital Anomalies of the Kidneys and Urinary Tracts (CAKUT)

2020· review· en· W3011486391 on OpenAlexafffund
Vasikar Murugapoopathy, Indra R. Gupta

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsMcGill University Health CentreMcGill University
FundersFondation des Etoiles
KeywordsNephronKidney developmentMedicineKidneyUrinary systemGestationFetusPhysiologyPediatricsPregnancyInternal medicineBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Congenital anomalies of the kidneys and urinary tracts (CAKUT) are disorders caused by defects in the development of the kidneys and their outflow tracts. The formation of the kidneys begins at week 3 and nephrogenesis continues until week 36, therefore, the kidneys and outflow tracts are susceptible to environmental risk factors that perturb development throughout gestation. Many genes have been implicated in kidney and outflow tract development, and mutations have been identified in patients with CAKUT. In severe cases of CAKUT, when the kidneys do not form, the fetus will not survive. However, in less severe cases, the baby can survive with combined kidney and outflow tract defects or they may only be identified in adulthood. In this review, we will cover the clinical presentation of CAKUT, its epidemiology, and its long-term outcomes. We will then discuss risk factors for CAKUT, including genetic and environmental contributions. Although severe CAKUT is rare, low nephron number is a much more common disorder with its effect on kidney function increasingly apparent as a person ages. Low nephron number appears to arise by the same mechanisms as CAKUT, but it differs in terms of the magnitude of the insult and the timing of when it occurs during gestation. By understanding the causes of CAKUT and low nephron number, we can begin to identify preventive treatments and establish clinical guidelines for how these patients should be followed.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0220.011

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 head0.356
Teacher spread0.317 · 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

Citations221
Published2020
Admission routes2
Has abstractyes

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