Prevalence, outcomes, and predictive factors: a systematic literature review to inform the development of EULAR Points to Consider for the definition of Difficult-to-Manage and Treatment-Refractory psoriatic arthritis
Bibliographic record
Abstract
Objectives: To perform a systematic literature review (SLR) to inform the EULAR Task Force "Points to Consider for the definitions of Difficult-to-Manage (D2M) and Treatment-Refractory (TR) psoriatic arthritis (PsA)". Methods: The SLR addressed 3 separate questions concerning the following: (1) prevalence, (2) outcomes, and (3) predictors of D2M/TR PsA. A search was conducted in MEDLINE (Ovid), Cochrane Database of Systematic Reviews, CENTRAL, EMBASE, and Epistemonikos from inception to March 3, 2024, as well as EULAR/ACR abstracts for 2023/24 and 2022/23, respectively. This systematic review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered on PROSPERO. Data are summarised descriptively. Meta-analysis was not possible due to significant heterogeneity and/or lack of high-quality evidence. Risk of bias (RoB) was assessed using the Newcastle-Ottawa Scale. Results: Overall, 70, 4, and 25 articles/abstracts were included for questions 1, 2, and 3, respectively. For question 1, 30/70 records had low/moderate RoB, with the prevalence of D2T PsA ranging from 5% to 80% depending on both the timepoint at which prevalence was measured and the definition of D2M/TR PsA applied, which varied significantly between studies. For question 2, all records had a high RoB; therefore, no conclusions could be drawn. For question 3, all records had a moderate RoB, and 84 categories of potential predictors were identified, with large differences in strength and direction of association between studies. Conclusions: Despite significant heterogeneity in the published literature regarding the scope, definition, long-term outcomes, and predictors of D2M/TR PsA, certain patterns emerged that helped shape the final EULAR Task Force recommendations.
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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 | 0.066 | 0.183 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".