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Record W2076375535 · doi:10.1038/ng.2723

Mutations in the gene encoding PDGF-B cause brain calcifications in humans and mice

2013· article· en· W2076375535 on OpenAlexaff
Annika Keller, Ana Westenberger, María Jesús Sobrido, María García-Murias, Aloysius Domingo, Renee Sears, Roberta R. Lemos, Andrés Ordóñez‐Ugalde, Gaël Nicolas, José Eriton Gomes da Cunha, Elisabeth J. Rushing, Michael Hugelshofer, Moritz C. Wurnig, Andres Kaech, Regina Reimann, Katja Lohmann, Valerija Dobričić, Ángel Carracedo, Igor Petrović, Janis M. Miyasaki, Irina Abakumova, Maarja Andaloussi Mäe, Elisabeth Raschperger, Mayana Zatz, Katja Zschiedrich, Jörg Klepper, Elizabeth Spiteri, José M. Prieto, Michael Preuß, Carmen Dering, Milena Janković, Martin Paucar, Per Svenningsson, Kioomars Saliminejad, Hamid Reza Khorram Khorshid, Ivana Novaković, Adriano Aguzzi, Andreas Boss, Isabelle Le Ber, Gilles Defer, Didier Hannequin, Dominique Campion, Daniel H. Geschwind, Giovanni Coppola, Christer Betsholtz, Christine Klein, João Ricardo Mendes de Oliveira

Bibliographic record

VenueNature Genetics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCaveolin-1 and cellular processes
Canadian institutionsUniversity of Toronto
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPDGFBPDGFRBBiologyMissense mutationPericytePDGFRACalcificationGeneticsPlatelet-derived growth factor receptorCancer researchMutationGenePathologyReceptorGrowth factorStromal cellMedicine

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.270
Teacher spread0.260 · 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 designBench or experimental
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

Citations328
Published2013
Admission routes1
Has abstractno

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