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Record W2955674936 · doi:10.3899/jrheum.190253

Association of Pharmacological Biomarkers with Treatment Response and Longterm Disability in Patients with Psoriatic Arthritis: Results from OUTPASS

2019· article· en· W2955674936 on OpenAlexvenueno aff
Meghna Jani, Hector Chinoy, Anne Barton

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
FundersVersus ArthritisNational Institute for Health and Care Research
KeywordsMedicinePsoriatic arthritisAdalimumabInternal medicineDrugBody mass indexProspective cohort studyCohortArthritisTumor necrosis factor alphaOncologyPhysical therapyPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify (1) whether tumor necrosis factor inhibitor (TNFi) drug levels/anti-drug antibodies (ADAb) are associated with treatment response and disability in patients with psoriatic arthritis (PsA); and (2) the factors associated with TNFi drug levels. METHODS: Patients were recruited from a national multicenter prospective cohort with longitudinal serum samples and 28-joint count Disease Activity Scores (DAS28)/Health Assessment Questionnaire (HAQ) measurement over 12 months. RESULTS: Adalimumab (ADA) drug levels were significantly associated with ΔDAS28 (β 0.055, 95% CI 0.011-0.099; p = 0.014) and inversely with HAQ over 12 months (β -0.022, 95% CI -0.043 to -0.00063). Factors significantly associated with ADA drug levels were ADAb levels and body mass index. CONCLUSION: Drug level testing in ADA-initiated PsA patients may be useful in determining treatment response/disability over 12 months.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.245
Teacher spread0.237 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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