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Record W4318926634 · doi:10.1007/s10875-023-01440-8

Autosomal Recessive Inflammatory Skin Disease Caused by a Novel Biallelic Loss-of-Function Variant in CARD11

2023· letter· en· W4318926634 on OpenAlexafffund
Amie Nguyen, Henry Y. Lu, Bradly M. Bauman, Gina Dabbah‐Krancher, Xijun Zhang, Gauthaman Sukumar, Joaquín Villar, Marita Bosticardo, Shefali Samant, Timothy James Maarup, Jerry C. Cheng, Neena Kapoor, David S. Cassarino, Joshua D. Milner, Luigi D. Notarangelo, Joseph A. Church, Javed Sheikh, Clifton L. Dalgard, Stuart E. Turvey, Andrew L. Snow

Bibliographic record

VenueJournal of Clinical Immunology · 2023
Typeletter
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchUniformed Services University of the Health SciencesGenome British ColumbiaBC Children's HospitalChildren's Hospital FoundationJeffrey Modell FoundationKillam TrustsU.S. Department of Defense
KeywordsLoss functionGeneticsMedical microbiologyDiseaseBiologyFunction (biology)MedicinePhenotypePathologyGeneImmunology

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)0.001
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.008
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 head0.079
GPT teacher head0.343
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations10
Published2023
Admission routes2
Has abstractno

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