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Record W2979981253 · doi:10.1016/j.ajhg.2019.09.013

Bi-allelic Variants in IQSEC1 Cause Intellectual Disability, Developmental Delay, and Short Stature

2019· article· en· W2979981253 on OpenAlexfundno aff
Muhammad Ansar, Hyung-Lok Chung, Ali Al‐Otaibi, Mohammad Nael Elagabani, Thomas A. Ravenscroft, Sohail Aziz Paracha, Ralf Scholz, Tayseer Abdel Magid, Muhammad Tahir Sarwar, Sayyed Fahim Shah, Azhar Ali Qaisar, Periklis Makrythanasis, Paul C. Marcogliese, Erik‐Jan Kamsteeg, Emilie Falconnet, Emmanuelle Ranza, Federico Santoni, Hesham Aldhalaan, Ali Alasmari, Eissa Faqeih, Jawad Ahmed, Hans‐Christian Kornau, Hugo J. Bellen, Stylianos E. Antonarakis

Bibliographic record

VenueThe American Journal of Human Genetics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institute of General Medical SciencesEuropean Research CouncilCanadian Institutes of Health ResearchIntellectual and Developmental Disabilities Research CenterDeutsche ForschungsgemeinschaftCarolinas HealthCare SystemCenter for Outcomes Research and Evaluation, Yale School of MedicineNational Institutes of HealthIndiana UniversityOffice of Research Infrastructure Programs, National Institutes of HealthHarvard UniversityCullen FoundationUniversity of IowaHoward Hughes Medical Institute
KeywordsBiologyGeneticsPhenotypeLoss functionShort statureAlleleHypotoniaGuanine nucleotide exchange factorGeneGTPaseEndocrinology

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.261
Teacher spread0.247 · 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

Citations37
Published2019
Admission routes1
Has abstractno

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