MétaCan
Menu
Back to cohort
Record W4400005116 · doi:10.1002/ajmg.a.63789

Genome sequencing identifies biallelic variants in <i>SCLT1</i> in a patient with syndromic nephronophthisis: Reflections on the <i>SCLT1</i>‐related ciliopathy spectrum

2024· article· en· W4400005116 on OpenAlexaff
E. Gillesse, Andrew Wade, Jillian S. Parboosingh, Ping Yee Billie Au, François P. Bernier, Ryan E. Lamont, A. Micheil Innes

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsCiliopathyCiliopathiesNephronophthisisBardet–Biedl syndromeGeneticsCiliumBiologyGenetic heterogeneityExome sequencingDiseaseJoubert syndromeMedicineComputational biologyBioinformaticsGenePathologyMutation

Abstract

fetched live from OpenAlex

Ciliopathies represent a major category of rare multisystem disease. Arriving at a specific diagnosis for a given patient is challenged by the significant genetic and clinical heterogeneity of these conditions. We report the outcome of the diagnostic odyssey of a child with obesity, renal, and retinal disease. Genome sequencing identified biallelic splice site variants in sodium channel and clathrin linker 1 (SCLT1), an emerging ciliopathy gene. We review the literature on all patients reported with biallelic SCLT1 variants highlighting a frequent clinical presentation that overlaps Bardet-Biedl and Senior-Loken syndromes. We also discuss current concepts in syndrome designation in light of these data.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.262
Teacher spread0.249 · 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 designCase report
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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part ASame topicGenetic and Kidney Cyst DiseasesFrench-language works237,207