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Record W2227403716 · doi:10.3899/jrheum.151296

The Need for Biological Outcomes for Biological Drugs in Psoriatic Arthritis

2016· letter· en· W2227403716 on OpenAlexvenueno aff
Ai Lyn Tan, Dennis McGonagle

Bibliographic record

VenueThe Journal of Rheumatology · 2016
Typeletter
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsPsoriatic arthritisMedicinePsoriasisEnthesitisContext (archaeology)Rheumatoid arthritisMagnetic resonance imagingDiseaseArthritisPsoriasis Area and Severity IndexInternal medicineImmunologyRadiology

Abstract

fetched live from OpenAlex

Perhaps no disease has been bedeviled more than psoriatic arthritis (PsA), with its proliferation of different clinical outcome measures. The development of these various measures to a large degree reflects both an inability to fully grasp disease pathogenic mechanisms and the difficulty in measuring them, especially in the context of the disease’s clinical heterogeneity. The absence of biological outcome measures, including an immunological or inflammation-related serum marker, makes the tomographic ability of magnetic resonance imaging (MRI) to directly measure inflammation arguably the most useful surrogate for PsA biological disease assessment. This is important because outcome measures to assess PsA are mushrooming (Figure 1). Figure 1. The use of clinical outcome measures in (A) psoriatic arthritis (PsA) compared to (B) rheumatoid arthritis (RA) over the decades28,30. There is an exponential increase in PsA outcome measures compared to RA because of the multidimensional nature of the disease, which makes it more difficult to assess adequately. The relatively more complex nature of PsA provides a challenge in developing a good tool that can measure joints, skin, nails, and entheses, resulting in the creation of multiple tools in the search for the ideal all-in-one outcome measure. Because biological drugs specifically target the immune system, MRI, with its ability to measure inflammation in different tissues, is the technique best able to measure most of the changes in PsA. The lists of outcome measures may not be exhaustive. MRI: magnetic resonance imaging; PASI: Psoriasis Area and Severity Index; MEI: Mander Enthesitis Index; SF-36: Medical Outcomes Study Short Form-36; HAQ: Health Assessment Questionnaire; BSA: body surface area; DLQI: Dermatology Life Quality Index; PsARC: Psoriatic Arthritis Response Criteria; DAPSA: Disease Activity in PsA; BASMI: Bath Ankylosing Spondylitis Metrology Index; BASFI: Bath Ankylosing Spondylitis Function Index; BASDAI: Bath Ankylosing Spondylitis Disease Activity Index; ACR: American College … Address correspondence to Dr. A.L. Tan, Leeds Institute of Rheumatic and Musculoskeletal Medicine, Chapel Allerton Hospital, Chapeltown Road, Leeds LS7 4SA, UK; E-mail: a.l.tan{at}leeds.ac.uk

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)0.050
metaresearch head score (Gemma)0.068
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: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0070.009
Open science0.0020.004
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0120.003

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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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
GenreEditorial

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

Citations5
Published2016
Admission routes1
Has abstractyes

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