Management of Dactylitis in Patients With Psoriatic Arthritis: An Updated Literature Review Informing the 2021 GRAPPA Treatment Recommendations
Bibliographic record
Abstract
OBJECTIVE: This literature review aimed to identify the most efficacious current interventions for dactylitis and provide up-to-date scientific evidence to support the 2021 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) recommendations on the management of psoriatic arthritis. METHODS: Original articles published from 2013 to 2020, registered in MEDLINE, Embase, and Cochrane Library, describing interventional trials and reporting dactylitis-related outcomes were included. The 20 members of the GRAPPA dactylitis group were divided into 9 subgroups according to treatment, and members of each group independently extracted data from articles/abstracts corresponding to their group by using a standardized data extraction form. RESULTS: Forty-nine publications were analyzed, representing 40 randomized clinical trials (RCTs) and including 16,752 patients. Dactylitis was assessed as a secondary outcome in 97.5% of these trials and more than 40% of RCTs did not employ a specific dactylitis measure or instrument. CONCLUSION: The emergence of agents with novel mechanisms of action in recent years, such as interleukin 17 (IL-17), IL-12/23, IL-23, and Janus kinase inhibitors, has significantly expanded the available treatment options for dactylitis. This article points out the lack of consensus regarding dactylitis assessment and the paucity of data concerning the effect of local steroid injections, nonsteroidal antiinflammatory drugs, and conventional disease-modifying antirheumatic drugs. Clinical trials evaluating the effect of these traditional and low-cost medications used to treat dactylitis should be encouraged.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".