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Record W1998901015 · doi:10.1016/j.ajhg.2013.05.028

Recessive TRAPPC11 Mutations Cause a Disease Spectrum of Limb Girdle Muscular Dystrophy and Myopathy with Movement Disorder and Intellectual Disability

2013· article· en· W1998901015 on OpenAlexafffund
Nina Bögershausen, Nassim Shahrzad, Jessica X. Chong, Jürgen‐Christoph von Kleist-Retzow, Daniela Stanga, Yun Li, François P. Bernier, Catrina M. Loucks, Radu Wirth, Erik G. Puffenberger, Robert A. Hegele, Julia Schreml, Gabriel Lapointe, Katharina Keupp, Christopher L. Brett, Rebecca L. Anderson, Andreas Hahn, A. Micheil Innes, Oksana Suchowersky, Marilyn B. Mets, Gudrun Nürnberg, D. Ross McLeod, Hölger Thiele, Darrel Waggoner, Janine Altmüller, Kym M. Boycott, Benedikt Schoser, Peter Nürnberg, Carole Ober, Raoul Heller, Jillian S. Parboosingh, Bernd Wollnik, Michael Sacher, Ryan E. Lamont

Bibliographic record

VenueThe American Journal of Human Genetics · 2013
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversity of OttawaConcordia UniversityWestern UniversityAlberta Children's HospitalUniversity of AlbertaMcGill UniversityChildren's Hospital of Eastern OntarioUniversity of Calgary
FundersInstitute of GeneticsNational Heart, Lung, and Blood InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentE-RareAlberta Children's Hospital FoundationBundesministerium für Bildung und ForschungNational Institutes of HealthChildren's Hospital FoundationMarch of Dimes Foundation
KeywordsMuscular dystrophyLimb-girdle muscular dystrophyMyopathyPhysical medicine and rehabilitationMedicineMovement disordersDiseaseIntellectual disabilityMutationGeneticsPsychiatryInternal medicineBiologyGene

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.015
GPT teacher head0.253
Teacher spread0.238 · 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 designObservational
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

Citations120
Published2013
Admission routes2
Has abstractno

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