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Record W3090167646 · doi:10.1038/s41588-020-0695-1

Mutations disrupting neuritogenesis genes confer risk for cerebral palsy

2020· article· en· W3090167646 on OpenAlexaff
Sheng Chih Jin, Sara A. Lewis, Somayeh Bakhtiari, Xue Zeng, Michael C. Sierant, Sheetal Shetty, Sandra M. Nordlie, Aureliane Elie, Mark Corbett, Bethany Y. Norton, Clare L. van Eyk, Shozeb Haider, Brandon S. Guida, Helen Magee, James H. Liu, Stephen F. Pastore, John B. Vincent, Janice Brunstrom-Hernandez, Antigone Papavasileiou, Michael Fahey, Jesia G. Berry, Kelly Harper, Chongchen Zhou, Junhui Zhang, Boyang Li, Hongyu Zhao, Jennifer Heim, Dani L. Webber, Mahalia S. B. Frank, Lei Xia, Yiran Xu, Dengna Zhu, Bohao Zhang, Amar H. Sheth, James Knight, Christopher Castaldi, Irina R. Tikhonova, Francesc López‐Giráldez, Boris Keren, Sandra Whalen, Julien Buratti, Diane Doummar, Megan Cho, Kyle Retterer, Francisca Millan, Yangong Wang, Jeff L. Waugh, Lance H. Rodan, Julie S. Cohen, Ali Fatemi, Angela E. Lin, J. P. Phillips, Timothy Feyma, Suzanna C. MacLennan, Spencer Vaughan, Kylie Crompton, Susan Reid, Dinah Reddihough, Qing Shang, Chao Gao, Iona Novak, Nadia Badawi, Yana A. Wilson, Sarah McIntyre, Shrikant Mane, Xiaoyang Wang, David J. Amor, Daniela C. Zarnescu, Qiongshi Lu, Qinghe Xing, Changlian Zhu, Kaya Bilgüvar, Sergio Padilla‐Lopez, Richard P. Lifton, Jozef Gécz, Alastair H. MacLennan, Michael C. Kruer

Bibliographic record

VenueNature Genetics · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsCentre for Addiction and Mental Health
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteDoris Duke Charitable FoundationNational Institute of Neurological Disorders and StrokeNational Human Genome Research InstituteU.S. Department of Health and Human Services
KeywordsBiologyExome sequencingRHOBGeneticsMutationExomeGene

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 score0.572
Threshold uncertainty score0.654

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 studies0.0000.000
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 head0.022
GPT teacher head0.297
Teacher spread0.275 · 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

Citations175
Published2020
Admission routes1
Has abstractno

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