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Record W2473456870 · doi:10.1002/ajmg.a.37831

Diagnosis of Van den Ende–Gupta syndrome: Approach to the Marden–Walker‐like spectrum of disorders

2016· article· en· W2473456870 on OpenAlexafffund
Karen Y. Niederhoffer, Somayyeh Fahiminiya, Patrice Eydoux, John Mawson, Gen Nishimura, Loydie A. Jerome‐Majewska, Millan S. Patel

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2016
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsMcGill Genome CentreMcGill UniversityUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchMcGill University
KeywordsExome sequencingFrameshift mutationDifferential diagnosisMedicineContext (archaeology)Intellectual disabilityPediatricsGeneticsExonMutationPathologyBiologyGenePsychiatry

Abstract

fetched live from OpenAlex

Marden-Walker syndrome is challenging to diagnose, as there is significant overlap with other multi-system congenital contracture syndromes including Beals congenital contractural arachnodactyly, D4ST1-Deficient Ehlers-Danlos syndrome (adducted thumb-clubfoot syndrome), Schwartz-Jampel syndrome, Freeman-Sheldon syndrome, Cerebro-oculo-facio-skeletal syndrome, and Van den Ende-Gupta syndrome. We discuss this differential diagnosis in the context of a boy from a consanguineous union with Van den Ende-Gupta syndrome, a diagnosis initially confused by the atypical presence of intellectual disability. SNP microarray and whole exome sequencing identified a homozygous frameshift mutation (p.L870V) in SCARF2 and predicted damaging mutations in several genes, most notably DGCR2 (p.P75L) and NCAM2 (p.S147G), both possible candidates for this child's intellectual disability. We review distinguishing features for each Marden-Walker-like syndrome and propose a clinical algorithm for diagnosis among this spectrum of disorders. © 2016 Wiley Periodicals, Inc.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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 designOther design
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

Citations13
Published2016
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Medical Genetics Part ASame topicNeurogenetic and Muscular Disorders ResearchFrench-language works237,207