MétaCan
Menu
Back to cohort
Record W2919268190 · doi:10.1038/nn.3688

Mutations in the Matrin 3 gene cause familial amyotrophic lateral sclerosis

2014· article· en· W2919268190 on OpenAlexaff
Janel O. Johnson, Erik P. Pioro, Ashley Boehringer, Ruth Chia, Howard Feit, Alan E. Renton, Hannah A. Pliner, Yevgeniya Abramzon, Giuseppe Marangi, Brett J Winborn, J. Raphael Gibbs, Michael A. Nalls, Sarah Morgan, Maryam Shoai, John Hardy, Alan Pittman, Richard W. Orrell, Andrea Malaspina, Katie Sidle, Pietro Fratta, Matthew B. Harms, Robert H. Baloh, Alan Pestronk, Conrad C. Weihl, Ekaterina Rogaeva, Lorne Zinman, Vivian E. Drory, Giuseppe Borghero, Gabriele Mora, Andrea Calvo, Jeffrey D. Rothstein, Carsten Drepper, Michael Sendtner, Andrew Singleton, J. Paul Taylor, Mark Cookson, Gabriella Restagno, Mario Sabatelli, Robert Bowser, Adriano Chiò, Bryan J. Traynor

Bibliographic record

VenueNature Neuroscience · 2014
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreOccupational Cancer Research CentreUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institute on AgingNational Institutes of HealthWellcome Trust
KeywordsAmyotrophic lateral sclerosisFrontotemporal dementiaTARDBPBiologyMotor neuronGeneGeneticsMutationRNA-binding proteinExome sequencingNeuroscienceRNADiseaseDementiaMutantMedicineSpinal cordSOD1Pathology

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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.315
Teacher spread0.276 · 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.

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

Citations443
Published2014
Admission routes1
Has abstractno

Explore more

Same venueNature NeuroscienceSame topicAmyotrophic Lateral Sclerosis ResearchFrench-language works237,207