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Record W4400871894 · doi:10.1007/s44162-024-00045-y

Woodhouse-Sakati syndrome: genotype–phenotype review and case of intra-familial heterogeneity

2024· article· en· W4400871894 on OpenAlexaff
Victor Wakim, Mohammad El Dassouki, Ahlam Azar, Abeer J. Hani, Cybel Mehawej, Éliane Chouery, Marie-Jeanne Baroudi, Gerard Wakim

Bibliographic record

VenueJournal of Rare Diseases · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhenotypeGenotypeGeneticsGenetic heterogeneityMedicineBiologyGene

Abstract

fetched live from OpenAlex

Abstract Woodhouse-Sakati syndrome (WSS) is a rare eponymous disease described by Drs. Woodhouse and Sakati in 1983 as a syndrome of hypogonadism, alopecia, diabetes mellitus, intellectual disability, and ECG abnormalities. A couple of years later, a variant in the gene DCAF17 (DDB1 and CUL4-associated factor 17) was labeled as the founder mutation in most cases of WSS in the Arabian Peninsula and the Middle East. Reports around the world started to emerge on variable presentations of the syndrome, expanding its phenotypic spectrum. In addition, the discovery of new variants in the same gene grew our understanding of this multi-systemic syndrome. Genotype and phenotype expansion is increasing with the growing number of diagnosed cases owing to the availability and advances in clinical genetic testing. This review describes the current understanding of the DCAF17 gene with its molecular implication in WSS. We also provide an extensive analysis of the documented genetic changes associated with the syndrome, describing the geographical prevalence of these genetic variations. Additionally, we examine the disorder’s extensive manifestations and clinical presentations and describe a case of intra-familial phenotypic heterogeneity.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.337
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.027
GPT teacher head0.295
Teacher spread0.268 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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