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Record W4417510897 · doi:10.1177/22143602251408154

2nd NMD4C basic research summer school on promoting standardized protocols to advance translational research in neuromuscular disorders

2025· article· en· W4417510897 on OpenAlexafffundabout
Adrien Rihoux, Emma R Sutton, J. K. Lee, Luke D. Flewwelling, Madison C. Garibotti, Homira Osman, Arthur J. Cheng, Anthony Scimè, Christopher G. R. Perry, Natasha C. Chang, Rashmi Kothary, Jean‐Philippe Leduc‐Gaudet

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversité du Québec à Trois-RivièresUniversity of OttawaMcGill UniversityMuscular Dystrophy CanadaYork UniversityOttawa HospitalArmand Frappier MuseumUniversité de Montréal
FundersInstitute of Musculoskeletal Health and Arthritis
KeywordsBasic researchTranslational researchNeuromuscular diseaseExperimental researchResearch programTranslational scienceMuscle diseaseBasic science

Abstract

fetched live from OpenAlex

The Neuromuscular Disease Network for Canada aims to accelerate research and improve care for individuals living with neuromuscular disorders by connecting basic scientists, clinicians, and trainees nationwide. As part of this mission, the network held its second Basic Research Summer School (May 7-8, 2025, York University, Toronto), integrating patient and caregiver perspectives with advanced scientific sessions and full-day methodological workshops. The program emphasized experimental rigor, reproducibility, and collaboration across research pillars, while launching the Basic Science Trainee Committee and Canada's first national, publicly accessible database of standard operating procedures for muscle research. This trainee-led, reproducibility-focused model of Summer School provides a transferable framework for building standardized preclinical capacity across international neuromuscular disorders research networks.

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)0.002
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 score0.680
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.046
GPT teacher head0.425
Teacher spread0.379 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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