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POS0462 HYDROXYCHLOROQUINE REDUCES THE TITERS OF ANTI-DOMAIN 1 ANTIBODIES OVER TIME IN PATIENTS WITH PERSISTENTLY POSITIVE ANTIPHOSPHOLIPID ANTIBODIES: RESULTS FROM THE APS ACTION CLINICAL DATABASE AND REPOSITORY (“REGISTRY”)

2022· article· en· W4283692802 on OpenAlexafffund
Cecilia Beatrice Chighizola, F. Pregnolato, D. Andrade, M. Tektonidou, Savino Sciascia, Vittorio Pengo, A. Ugarte, H. M. Belmont, Maria Gerosa, P. Fortin, C. López-Pedrera, Z. Zhang, Tatsuyaa Atsumi, Guilherme Ramires de Jesús, N. Kello, D. W. Branch, L. Andreoli, Dario Wahl, M. A. Petri, E. Rodríguez Almaraz, R. Cervera, G. Pons Estel, J. Knight, R. Willis, M. Barber, B. Artim Esen, M. Efthymiou, D. Erkan, M. L. Bertolaccini

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of CalgaryUniversité Laval
FundersPeking UniversityDirectorate for Biological SciencesUniversidade do Estado do Rio de JaneiroHokkaido UniversityUniversità degli Studi di TorinoUniversity College LondonUniversidad de CórdobaNorthwell HealthYork UniversityUniversità degli Studi di BresciaPeking University First HospitalUniversità degli Studi di MilanoEuskal Herriko UnibertsitateaEuropean CommissionJohns Hopkins UniversityKing's College LondonUniversité LavalHospital for Special SurgeryUniversità degli Studi di PadovaIntermountain Healthcare
KeywordsMedicineAntiphospholipid syndromeHydroxychloroquineCohortTiterInternal medicineAntibodyDatabaseImmunologyDisease

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)0.002
metaresearch head score (Gemma)0.004
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.320
Teacher spread0.285 · 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

Citations2
Published2022
Admission routes2
Has abstractno

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