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POS0111 MORE METICULOUSLY FOLLOWING TREAT-TO-TARGET IN RA DOES NOT LEAD TO LESS RADIOGRAPHIC PROGRESSION: A LONGITUDINAL ANALYSIS IN BIODAM

2022· article· en· W4290997069 on OpenAlexafffund
S. Ramiro, R. B. M. Landewé, D. van der Heijde, A. Sepriano, O. Fitzgerald, M. Østergaard, J. Homik, O. Elkayam, C. Thorne, M. Larché, Gianfranco Ferraccioli, M. Backhaus, G. Boire, Bernard Combe, T. Schaeverbeke, A. Saraux, M. Dougados, M. Rossini, M. Govoni, L. Sinigaglia, A. Cantagrel, C. Allaart, C. Barnabe, C. Bingham, D. van Schaardenburg, H. B. Hammer, R. Dadashova, E. Hutchings, J. Paschke, Walter P. Maksymowych

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversity of CalgaryCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanMcMaster UniversitySouthlake Regional Health CenterCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of TorontoCentre Hospitalier Universitaire de SherbrookeUniversity of Alberta
FundersAssistance publique-Hôpitaux de ParisUniversité Paris DescartesUniversità degli Studi di FerraraInstitut National de la Santé et de la Recherche MédicaleUniversità degli Studi di VeronaUniversité de BordeauxCentre Hospitalier Régional Universitaire de MontpellierJohns Hopkins UniversityUniversité de SherbrookePfizerDiakonhjemmetEli Lilly and Company
KeywordsMedicineRadiographyLead (geology)Surgery

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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.028
GPT teacher head0.350
Teacher spread0.322 · 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

Citations1
Published2022
Admission routes2
Has abstractno

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