Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".