The relationship between patient acceptable symptom state and disease activity in patients with psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: The Psoriatic Arthritis Disease Activity Score (PASDAS) and Disease Activity Index for Psoriatic Arthritis (DAPSA) are composite PsA disease activity measures. We sought to identify the PASDAS and DAPSA cut-off points consistent with patient acceptable symptom state (PASS), the threshold of symptoms beyond which patients consider themselves well, and examine PASS across published PASDAS and DAPSA thresholds for low, moderate and high disease activity. METHODS: We used a standard protocol including physician assessment and patient-reported outcomes to prospectively record measures required to calculate PASDAS and DAPSA. We identified PASS thresholds for the PASDAS and DAPSA using receiver operating characteristics curve analyses. We assessed the frequency of reporting acceptable symptom state across disease activity thresholds for PASDAS and DAPSA scores. RESULTS: A total of 229 patients (58.5% male, mean age 55.5 years, mean disease duration 17.1 years) were recruited. The PASS threshold for the PASDAS was 3.79 [area under the curve (AUC) 0.86, sensitivity 0.75, specificity 0.82] and for the DAPSA was 11.10 (AUC 0.91, sensitivity 0.89, specificity 0.82). With the PASDAS, 90% of patients defined as having low disease activity considered their symptom state acceptable, compared with 55% and 17% among those with moderate and high disease activity, respectively. With the DAPSA, 98% of patients in disease remission considered their symptom state acceptable compared with 85, 22 and 18% among those with low, moderate and high disease activity, respectively. CONCLUSION: We have defined PASS thresholds for PASDAS and DAPSA. The PASDAS target for low disease activity and DAPSA targets of low disease activity or remission align well with PASS.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".