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Record W2971203796 · doi:10.1016/j.ajhg.2019.07.019

Redefining the Etiologic Landscape of Cerebellar Malformations

2019· article· en· W2971203796 on OpenAlexaff
Kimberly A. Aldinger, Andrew E. Timms, Zachary Thomson, Ghayda Mirzaa, James T. Bennett, Alexander Rosenberg, Charles M. Roco, Matthew Hirano, Fatima Abidi, Parthiv Haldipur, Chi Vicky Cheng, Sarah Collins, Kaylee Park, Jordan Zeiger, Lynne M. Overmann, Fowzan S. Alkuraya, Leslie G. Biesecker, Stephen R. Braddock, Sara Cathey, Megan T. Cho, Brian Hon‐Yin Chung, David B. Everman, Yuri A. Zárate, Julie R. Jones, Charles E. Schwartz, Amy Goldstein, Robert J. Hopkin, Ian D. Krantz, Roger L. Ladda, Kathleen A. Leppig, Barbara McGillivray, Susan L. Sell, Katherine Wusik, Joseph G. Gleeson, Deborah A. Nickerson, Michael J. Bamshad, Dianne Gerrelli, Steven Lisgo, Georg Seelig, Gisele E. Ishak, A. James Barkovich, Cynthia J. Curry, Ian A. Glass, Kathleen J. Millen, Dan Doherty, William B. Dobyns

Bibliographic record

VenueThe American Journal of Human Genetics · 2019
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsChildren's & Women's Health Centre of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Human Genome Research InstituteNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institutes of HealthIntellectual and Developmental Disabilities Research CenterNational Medical Research CouncilDandy-Walker AllianceWellcome Trust
KeywordsExome sequencingCerebellar hypoplasia (non-human)BiologyCerebellumGenetic testingExomeGeneticsMedicinePathologyNeuroscienceMutationGene

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)0.002
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.002
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.003
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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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

Citations97
Published2019
Admission routes1
Has abstractno

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