Residual Disease Activity and Associated Factors in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Remission or low disease activity should be the treatment target of psoriatic arthritis (PsA). However, residual disease activity (RDA) in some domains could persist. The aim of this study was to assess RDA and its associated factors in a group of patients with PsA. METHODS: Patients with PsA were enrolled if they satisfied ClASsification for Psoriatic ARthritis (CASPAR) criteria with > 6 months' followup and achieved a status of low disease activity (LDA), minimal disease activity (MDA), or remission [Disease Activity Index for PsA (DAPSA) remission or very low disease activity (VLDA)]. RDA was assessed by the percentage of patients who had, although in LDA or remission, tender and/or swollen joints > 1, Leeds Enthesitis Index > 1, Health Assessment Questionnaire > 0.5, Psoriasis Area Severity Index (PASI) > 1, patient's global assessment > 20, physician visual analog scale (VAS) > 20, and VAS pain > 15. Associated factors of RDA were also assessed. RESULTS: Of 113 enrolled patients, 78 (69%) were in MDA. Moreover, DAPSA remission was observed in 46 (40.7%) while VLDA only in 32 (28.3%) of patients with PsA. VLDA seems to be the most stringent criterion, with a minimal RDA only in the VAS physician in 1 patient (3.1%) and none in the different domains, while patients in MDA had RDA in tender joints (14.1%), VAS pain (29.4%) and PASI > 1 or body surface area (BSA) > 3% (17.9%). Of note, although patients in DAPSA remission show a very low rate of RDA in almost all domains, 12 (26%) of them show a PASI > 1 or BSA > 3%. Finally, LDA shows RDA in higher percentages, mainly in patient-reported outcomes, tender joints, and skin domain. CONCLUSION: RDA can be recognized in patients with PsA. VLDA seems to be the most stringent composite index to identify patients in the absence of RDA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".