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Treating juvenile idiopathic arthritis to target: recommendations of an international task force

2018· article· en· W2797862713 on OpenAlexaff
Angelo Ravelli, Alessandro Consolaro, Gerd Horneff, Ronald M. Laxer, Daniel J. Lovell, Nico Wulffraat, Jonathan Akikusa, Sulaiman M. Al‐Mayouf, Jordi Antón, Tadej Avčin, Roberta Berard, Michael W. Beresford, Rubén Burgos‐Vargas, Rolando Cimaz, Fabrizio De Benedetti, Erkan Demirkaya, Dirk Foell, Yasuhiko Itoh, Pekka Lahdenne, Esi M. Morgan, Pierre Quartier, Nicolino Ruperto, Ricardo Russo, Claudia Saad‐Magalhães, Sujata Sawhney, Christiaan Scott, Susan Shenoi, Joost F. Swart, Yosef Uziel, Sebastiaan J. Vastert, Josef S Smolen

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2018
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsWestern UniversitySickKids FoundationHospital for Sick ChildrenLondon Health Sciences CentreUniversity of Toronto
FundersAbbVie
KeywordsMedicineTimelineTask forceDelphi methodSet (abstract data type)ArthritisTask (project management)DelphiPhysical therapyInternal medicineComputer science

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)0.054
metaresearch head score (Gemma)0.049
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 score0.056
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.049
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0050.004
Science and technology studies0.0050.004
Scholarly communication0.0070.004
Open science0.0090.006
Research integrity0.0280.030
Insufficient payload (model declined to judge)0.0060.005

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 head0.036
GPT teacher head0.350
Teacher spread0.313 · 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

Citations357
Published2018
Admission routes1
Has abstractno

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