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Development of the autoinflammatory disease damage index (ADDI)

2016· article· en· W2548885112 on OpenAlexaff
Nienke M. ter Haar, Kim V. Annink, Sulaiman M. Al‐Mayouf, Gayane Amaryan, Jordi Antón, Karyl S. Barron, Susanne M. Benseler, Paul Brogan, Luca Cantarini, Marco Cattalini, Alexis‐Virgil Cochino, Fabrizio De Benedetti, Fatma Dedeoğlu, Adriana A. de Jesus, Ornella Della Casa Alberighi, Erkan Demirkaya, Pavla Doležalová, Karen Durrant, Giovanna Fabio, Romina Gallizzi, Raphaela Goldbach‐Mansky, É. Hachulla, Véronique Hentgen, Troels Herlin, Michaël Hofer, Hal M. Hoffman, Antonella Insalaco, Annette Jansson, Tilmann Kallinich, Isabelle Koné‐Paut, А. Л. Козлова, Jasmin Kuemmerle‐Deschner, Helen J. Lachmann, Ronald M. Laxer, Alberto Martini, Susan Nielsen, И. П. Никишина, Amanda K. Ombrello, Seza Özen, Efimia Papadopoulou‐Alataki, Pierre Quartier, Donato Rigante, Ricardo Russo, Anna Simon, Maria Trachana, Yosef Uziel, Angelo Ravelli, Marco Gattorno, Joost Frenkel

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenAlberta Children's Hospital
FundersNational Institute of Allergy and Infectious DiseasesSwedish Orphan BiovitrumRosetrees TrustExecutive Agency for Health and ConsumersNovartis PharmaRegeneron Pharmaceuticals
KeywordsMedicineIndex (typography)DiseaseInternal medicineWorld Wide Web

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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

Citations76
Published2016
Admission routes1
Has abstractno

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