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Record W2981508973 · doi:10.1016/j.nmd.2019.10.005

239th ENMC International Workshop: Classification of dermatomyositis, Amsterdam, the Netherlands, 14–16 December 2018

2019· article· en· W2981508973 on OpenAlexaff
Andrew L. Mammen, Yves Allenbach, Werner Stenzel, Olivier Benvéniste, Olivier Benveniste, Jan De Bleecker, Olivier Boyer, Livia Casciola‐Rosen, Lisa Christopher‐Stine, Jan Damoiseaux, Cyril Gitiaux, Manabu Fujimoto, Janine A. Lamb, Océane Landon‐Cardinal, Ingrid E. Lundberg, Ichizo Nishino, Josefine Radke, Albert Selva-O’Callaghan, Jiří Vencovský, Guochun Wang, Lucy R. Wedderburn, Victoria P. Werth

Bibliographic record

VenueNeuromuscular Disorders · 2019
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité du Québec à Montréal
FundersVersus ArthritisNational Institute of Arthritis and Musculoskeletal and Skin DiseasesEuropean Neuromuscular CentreMedical Research CouncilNational Institute for Health and Care Research
KeywordsDermatomyositisAntisynthetase syndromeMedicineAutoantibodyDermatologyJuvenile dermatomyositisImmunologyAntibody

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0440.023

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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 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

Citations299
Published2019
Admission routes1
Has abstractno

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