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Record W4224879671 · doi:10.1016/j.jaad.2022.04.028

Cross-sectional characteristics of pediatric-onset discoid lupus erythematosus: Results of a multicenter, retrospective cohort study

2022· article· en· W4224879671 on OpenAlexaff
Nnenna Ezeh, Kaveh Ardalan, Kevin A. Buhr, Cordellia Nguyen, Omnia Ahmed, Stacy P. Ardoin, Virginia Barton, S. Bell, Heather A. Brandling‐Bennett, Leslie Castelo‐Soccio, Yvonne E. Chiu, Benjamin F. Chong, Dominic O. Co, Irene Lara‐Corrales, Amber Cintosun, Megan L. Curran, Lucia Z. Diaz, Scott A. Elman, Esteban Fernández Faith, María Teresa García‐Romero, J. Grossman‐Kranseler, Marcia Hogeling, Andrew Hudson, Ruth Hunt, Erin Ibler, Marco Marques, Reesa L. Monir, Vikash S. Oza, Amy S. Paller, Elana Putterman, Pamela Rodríguez-Salgado, Jennifer J. Schoch, Allison Truong, Jia‐Bin Wang, L Wine Lee, Ruth Ann Vleugels, Marisa S. Klein‐Gitelman, Emily von Scheven, Victoria P. Werth, Lisa M. Arkin

Bibliographic record

VenueJournal of the American Academy of Dermatology · 2022
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersEMD SeronoNational Institutes of HealthPrincipia BiopharmaGeorgia Clinical and Translational Science AllianceNational Center for Advancing Translational SciencesPediatric Dermatology Research AllianceBiogen
KeywordsMedicineDiscoid lupus erythematosusSystemic lupus erythematosusRheumatologyRetrospective cohort studyInternal medicineAnti-nuclear antibodyLupus erythematosusCohortDermatologyConnective tissue diseaseAutoimmune diseaseImmunologyDiseaseAntibodyAutoantibody

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.346
Teacher spread0.323 · 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

Citations6
Published2022
Admission routes1
Has abstractno

Explore more

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