Variability of Psoriatic Arthritis Impact of Disease questionnaire (PsAID12) thresholds in psoriatic arthritis: data from the ReFlaP study
Bibliographic record
Abstract
OBJECTIVE: To explore thresholds for the Psoriatic Arthritis (PsA) Impact of Disease questionnaire (PsAID12) score against disease activity measures in an observational setting, in patients with PsA. METHODS: The baseline data from the ReFlaP observational, prospective, multicentre and international study were used (NCT03119805). Cutoffs for PsAID12 were determined against disease activity scores, defining disease impact states (i.e. remission, low impact, moderate impact and high impact). Statistics used to assess the optimal cutoff point included Youden's index and the 75th percentile method, with external anchors (i.e. Disease Activity index for Psoriatic Arthritis [DAPSA], very low disease activity [VLDA]/minimal disease activity [MDA] and single questions for both patients and physicians) serving as gold standards. The diagnostic performance of these cutoffs was evaluated using receiver operating characteristic (ROC) curve analyses. RESULTS: A total of 410 patients were analysed. Mean (s.d.) PsAID12 score was 3.4 (2.5). The prevalence of remission varied between 12.4% and 36.1%, while low disease activity ranged from 37.8% to 59.8%. PsAID12 performed well against external anchors, with high areas under the ROC curves ranging from 0.75 to 0.94. Using the DAPSA as external anchor, the proposed PsAID12 cutoffs were <1.7 for remission, ≥1.7 to ≤3.1 for low impact, >3.1 to <4.8 for moderate impact and ≥4.8 for high impact. Compared with composite scores, patient and physician opinions performed less stringently. CONCLUSION: This study established cutoffs for PsAID12 in a clinical practice observational population, corresponding to remission and varying levels of disease impact. However, these proposed cutoffs need further validation, and an expert consensus is essential to confirm the most accurate thresholds for future use.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".