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Record W2970786401 · doi:10.1002/ajmg.c.31741

Collaborating to advance interdisciplinary care for individuals with arthrogryposis

2019· article· en· W2970786401 on OpenAlexaff
Noémi Dahan‐Oliel, Judith G. Hall

Bibliographic record

VenueAmerican Journal of Medical Genetics Part C Seminars in Medical Genetics · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsBC Children's HospitalChildren's & Women's Health Centre of British ColumbiaMcGill UniversityShriners Hospitals for Children - Canada
Fundersnot available
KeywordsArthrogryposisArthrogryposis multiplex congenitaMedicineMultidisciplinary approachPsychologySociologySocial scienceSurgery

Abstract

fetched live from OpenAlex

This Special Issue on Interdisciplinary Care in Arthrogryposis highlights a collection of articles spanning topics in interdisciplinary care, genetic discoveries, and clinical research. An international group of clinicians and researchers from various backgrounds who attended the "3rd International Symposium on Arthrogryposis", held in Philadelphia, September 24-26, 2018, were invited to contribute to this issue. The goal of the 2018 Symposium and of this Special Issue is to provide momentum to advancing evidence-based practice and research in arthrogryposis, by working collaboratively with adults and families of children with arthrogryposis, clinicians, and researchers. The contents of this issue cover a range of topics from defining and classifying arthrogryposis multiplex congenita to early detection, rehabilitation, and orthopedic management, advances in genetic pathways, patient registries, autopsy guidelines, and research findings in the pediatric and adult populations with arthrogryposis. We hope that this issue provides an overview as well as new knowledge on arthrogryposis to generate more conversations at the international level, and advance care and research for individuals with arthrogryposis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.010
GPT teacher head0.358
Teacher spread0.348 · 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
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

Citations4
Published2019
Admission routes1
Has abstractyes

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