Treating Psoriatic Arthritis to Target: Defining the Psoriatic Arthritis Disease Activity Score That Reflects a State of Minimal Disease Activity
Bibliographic record
Abstract
OBJECTIVE: The Psoriatic Arthritis Disease Activity Score (PASDAS) is a composite disease activity measure (range 0-10) for psoriatic arthritis (PsA). We aimed to validate a cutoff value of PASDAS that defines minimal disease activity (MDA) state, as well as to validate previously defined PASDAS cutoffs for low and high disease activity. METHODS: Patients were prospectively recruited from the University of Toronto PsA clinic according to a standard protocol, and variables necessary to complete the PASDAS and the MDA were collected. Receiver-operating characteristic (ROC) curve analysis determined the optimal PASDAS cutoff discriminating patients in MDA state from those not in MDA. Previously proposed PASDAS disease activity cutoff scores were validated by determining the proportion of patients requiring treatment escalation, a surrogate of active disease, in each of low, moderate, and high disease activity groups. RESULTS: One hundred seventy-eight patients [53.9% male, mean PASDAS 3.29 (SD 1.29), 47.8% in MDA] were recruited. ROC curve analysis identified a PASDAS score of 3.2 as the point that maximized the sensitivity and specificity for MDA based on 5 of 7 criteria (sensitivity 88%, specificity 92%, area under the curve 0.96). For MDA based on meeting 6 of 7 and 7 of 7 criteria, PASDAS scores of 2.6 and 2.1 maximized sensitivity and specificity, respectively. An increasing proportion of patients from low to moderate to high disease activity groups required treatment escalation, increasing from 8.1% to 42% to 67%, respectively. CONCLUSION: A PASDAS score < 3.2 reflects MDA. This study has externally validated PASDAS cutoff scores previously proposed to differentiate between low, moderate, and high disease activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".