Informing Response Criteria for Psoriatic Arthritis (PsA). II: Further Considerations and a Proposal — The PsA Joint Activity Index
Bibliographic record
Abstract
OBJECTIVE: To develop a recommended measure of response for use in psoriatic arthritis (PsA) clinical trials and observational cohort studies reflecting joint involvement. METHODS: Previously, we used data from phase III randomized placebo-controlled trials of anti-tumor necrosis factor (TNF) agents to determine models, based primarily on statistical considerations but with some clinical input when necessary, that best distinguish drug-treated from placebo-treated patients. For the same data, we examine response criteria currently used for PsA and logistic regression models based on the individual components of these response criteria. Comparison with our previously developed models, based primarily on statistical consideration, is made. RESULTS: A simplified score, the PsA Joint Activity Index (PsAJAI), based on components of the ACR30, performed better than the ACR20 and PsARC, and comparable to our previously developed models. The PsAJAI is a weighted sum of 30% improvement in core measures with weights of 2 given to the joint count measure, the C-reactive protein laboratory measure, and the physician global assessment of disease activity measure. Weights of 1 should be given to the remaining 30% improvement measures including pain, patient global assessment of disease activity, and the Health Assessment Questionnaire. CONCLUSION: We recommend the PsAJAI be used as an outcome measure for assessing joint disease response in PsA clinical trials.
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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 | 0.127 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".