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Record W4415641091 · doi:10.1186/s40035-025-00516-2

Shared burden of ultra-rare genetic variants across a spectrum of motor neuron diseases

2025· letter· en· W4415641091 on OpenAlexaff
Gang Wu, Wenan Chen, Joanne Wuu, Angita Jain, Jason R. Myers, Isabell Cordts, Evadnie Rampersaud, Jeannine M. Heckmann, Melissa Nel, Volkan Granit, Jeffrey Statland, Andrea Swenson, John Ravits, Corey T. McMillan, Lauren Elman, James B. Caress, Ted M. Burns, Erik P. Pioro, Jaya Trivedi, Jonathan N. Katz, Carlayne E. Jackson, Samuel Maiser, David Walk, Yuen T. So, Jacob L. McCauley, Matthew Baker, J. Paul Taylor, Stephan Züchner, Rosa Rademakers, Marka van Blitterswijk, Michael Benatar

Bibliographic record

VenueTranslational Neurodegeneration · 2025
Typeletter
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsUniversity of British ColumbiaVancouver Coastal Health
FundersNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeU.S. Food and Drug AdministrationNational Cancer InstituteNational Institutes of HealthRare Diseases Clinical Research NetworkTarget ALSWellcome TrustALS Association
KeywordsGenetic variantsNeurologyMotor neuronBroad spectrumNeuronGenetic variationMotor activity

Abstract

fetched live from OpenAlex

Emerging evidence suggests an intricate genetic architecture in motor neuron diseases (MNDs), involving not only monogenic causes but also, to varying extents, risk alleles and oligogenic or polygenic contributions [1-3].The potential for shared genetic risk across related diseases has motivated us to examine the contributions of rare variants in canonical and non-canonical diseaseassociated genes in a group of MNDs to understand the † Gang Wu and Michael Benatar contributed equally.

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)0.001
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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