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Record W2966640019 · doi:10.1002/ajmg.c.31730

The diagnostic workup in a patient with AMC: Overview of the clinical evaluation and paraclinical analyses with review of the literature

2019· review· en· W2966640019 on OpenAlexaff
Klaus Dieterich, Pauline Le Tanno, Eva Kimber, Pierre‐Simon Jouk, Judith G. Hall, Philip F. Giampietro

Bibliographic record

VenueAmerican Journal of Medical Genetics Part C Seminars in Medical Genetics · 2019
Typereview
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsArthrogryposis multiplex congenitaMuscle contractureMedicineAnterior Horn CellArthrogryposisPhysical medicine and rehabilitationBioinformaticsPathologySurgeryDiseaseBiology

Abstract

fetched live from OpenAlex

Arthrogryposis multiplex congenita, or AMC, is a clinical sign defined as congenital contractures of at least two joint levels. These joint contractures are always secondary to diminished fetal movement which can have numerous causes that affect any part of the anatomical structures implicated in movement: the central nervous system, the anterior horn cell, the nerve, the neuromuscular junction, the muscle, or the joint itself. Make a precise diagnosis of the cause in a patient with multiple joint contractures is therefore challenging. The aim of this article is to summarize the use and diagnostic value of common examinations and analyses performed postnatally in patients affected by AMC from a literature review. We also compare this data with results from our clinical practice. Even though it is difficult to give precise guidelines today, it appears that genetic studies, such as whole exome or genome analysis in all patients and chromosomal microarray analysis in patients with intellectual disability and AMC should be preferred as first tier investigations over EMG and muscle biopsy.

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)0.010
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.004
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 head0.131
GPT teacher head0.512
Teacher spread0.382 · 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.

Study designOther design
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

Citations26
Published2019
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Medical Genetics Part C Seminars in Medical GeneticsSame topicNeurogenetic and Muscular Disorders ResearchFrench-language works237,207