Development of an Instrument for Patient Self-assessment in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Due to the recent pandemic caused by the coronavirus disease 2019 (COVID-19), in-person scheduled rheumatology appointments in many countries have been reserved for urgent cases only. Here we report the development of a multidimensional, patient-completed disease assessment tool for use in psoriatic arthritis (PsA). METHODS: A focus group development and education method was used, followed by a paired observation design to assess feasibility and validity. The Psoriatic Arthritis Disease Activity Score (PASDAS) was used as the basis for the clinical assessments, but elements of this tool were modified during the focus group sessions. RESULTS: A preliminary tool assessed tender and swollen joint counts, enthesitis, dactylitis, area of skin involved by psoriasis, and scores for global disease activity, fatigue, and spinal pain. In parallel assessments, good agreement was found between subject and healthcare professional (HCP) assessors, although overall disease activity was low. CONCLUSION: A self-assessment tool for disease activity in PsA has been developed in conjunction with patients, demonstrating generally good agreement between patients and HCPs; however, further validation is needed before it can be recommended for clinical practice.
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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 | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".