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Record W2897045430 · doi:10.3233/jnd-180324

“Of Mice and Measures”: A Project to Improve How We Advance Duchenne Muscular Dystrophy Therapies to the Clinic

2018· article· en· W2897045430 on OpenAlexaff
Heather Gordish‐Dressman, Raffaella Willmann, Laura Dalle Pazze, Arati S. Kreibich, Maaike van Putten, Ahlke Heydemann, Laurent Bogdanik, Cathleen Lutz, Kay E. Davies, Alexis R. Demonbreun, Dongsheng Duan, David J. Elsey, So‐ichiro Fukada, Mahasweta Girgenrath, J. Patrick Gonzalez, Miranda D. Grounds, A. Nichols, Terry Partridge, Marco A. Passini, Francesca Sanarica, Frederick J. Schnell, Dominic J. Wells, Toshifumi Yokota, Courtney S. Young, Zhong Zhong, Christopher F. Spurney, Melissa J. Spencer, Annamaria De Luca, Kanneboyina Nagaraju, Annemieke Aartsma‐Rus

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Alberta
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institutes of HealthPfizerSarepta TherapeuticsU.S. Department of Defense
KeywordsDuchenne muscular dystrophymdx mouseMedicineMuscular dystrophyPathologicalNatural historyPreclinical researchBioinformaticsDiseaseInternal medicineDystrophinBiologyMedical physics

Abstract

fetched live from OpenAlex

A new line of dystrophic mdx mice on the DBA/2J (D2) background has emerged as a candidate to study the efficacy of therapeutic approaches for Duchenne muscular dystrophy (DMD). These mice harbor genetic polymorphisms that appear to increase the severity of the dystropathology, with disease modifiers that also occur in DMD patients, making them attractive for efficacy studies and drug development. This workshop aimed at collecting and consolidating available data on the pathological features and the natural history of these new D2/mdx mice, for comparison with classic mdx mice and controls, and to identify gaps in information and their potential value. The overall aim is to establish guidance on how to best use the D2/mdx mouse model in preclinical studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0090.003

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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
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

Citations39
Published2018
Admission routes1
Has abstractyes

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