Assessment of patients with psoriatic arthritis: A review of currently available measures
Bibliographic record
Abstract
To date, specific measures to assess disease activity in PsA have not been validated. The tools used for assessment of psoriatic peripheral joint disease have been borrowed from the assessment of RA. While, in general, some of these outcome measures perform well in PsA, the distinct distribution of joint involvement coupled with the unique patterns of synovitis and periarticular inflammation demand that these measures be appropriately and carefully adapted. In order to capture the wide variation that characterizes the clinical presentation seen among patients with PsA, a larger number of joints should be assessed. Moreover, because of the frequency and severity of back involvement in PsA, assessment of spinal mobility and pain is required. Many clinical methods used to assess axial disease in AS have not proven to be reliable, and even measures deemed appropriate in AS require validation in PsA. Specific assessment of common features of PsA, including dactylitis, enthesitis, and tendonitis, requires further study. In addition, available techniques for radiographic evaluation of patients with RA may not fully record the specific changes noted among patients with PsA. Moreover, the utility of newer imaging techniques in the assessment of joint and spinal inflammation in patients with PsA requires further study. Finally, it is important to evaluate whether the same instruments can be used for the various patterns of PsA. Rigorous validation of currently used techniques, combined with the development of new assessment tools, is urgently required to fully assess the therapeutic response in PsA. In our view, a consensus development of a core set of outcome domains, psychometric evaluation of candidate instruments in patients with PsA, and construction of response criteria from clinical trials that have used such instruments constitute the research agenda for PsA.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".