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Record W3024298841 · doi:10.1097/bor.0000000000000715

The management of enthesitis in clinical practice

2020· review· en· W3024298841 on OpenAlexaff
Sahil Koppikar, Lihi Eder

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2020
Typereview
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsEnthesitisMedicinePsoriatic arthritisClinical trialDiseasePhysical therapyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Enthesitis is a hallmark feature of the spondyloarthropathies (SpA). This review provides an overview of recent insights on diagnosis and management of enthesitis. RECENT FINDINGS: Recent studies support the use of imaging for diagnosis because of its higher sensitivity and specificity compared with clinical examination. Several new MRI and ultrasound scoring systems have been developed for enthesitis, which may facilitate the use of imaging in research. Enthesitis has been evaluated as a primary study outcome mainly in psoriatic arthritis (PsA); however, the use of different indices and definitions of improvement limits comparison across studies. There is very limited information about the efficacy of synthetic disease modifying antirheumatic drugs (DMARDs) for the treatment of enthesitis. In contrast, targeted and biologic DMARDs have all shown efficacy in treating enthesitis compared with placebo. There have been only a few head-to-head trials that compared two different cytokine inhibitors for the treatment of enthesitis. Preliminary data suggest that targeting IL-17 or IL12/23 may be more efficacious for controlling enthesitis than TNF inhibition. SUMMARY: Emerging data suggest interleukin-17 and 12/23 inhibitors may be the first choice in PsA patients with enthesitis. Further head-to-head studies are needed before making definitive recommendations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

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

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 head0.136
GPT teacher head0.479
Teacher spread0.343 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations6
Published2020
Admission routes1
Has abstractyes

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