Defining Low Disease Activity States in Psoriatic Arthritis using Novel Composite Disease Instruments
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between minimal disease activity (MDA) and the low disease activity cutoffs of the Psoriatic ArthritiS Disease Activity Score (PASDAS) and the Composite Psoriatic Disease Activity Index (CPDAI). METHODS: Data from the GRAPPA (Group for Research and Assessment of Psoriasis and Psoriatic Arthritis) composite exercise (GRACE) study were used for these analyses. Alternative definitions of low disease activity were used with 6/7 and 7/7 of MDA items, and a criteria set mandating the 2 articular items and 3/5 alternate items (MDA-joints). Two reference questions were used as anchors: physician's global opinion of MDA, and patient's opinion on their disease control. RESULTS: Substantial agreement was found between MDA, MDA-joints, PASDAS, and CPDAI. Compared to the 2 reference questions, the various definitions of low disease activity gave sensitivities that were generally worse than specificities, the latter being high (> 0.9) in most cases. Both PASDAS and CPDAI demonstrated good discrimination between the "low" and "high" disease activity states by all the MDA definitions. Using these data, with an MDA of 7/7 to define a very low disease cutoff, the corresponding values for PASDAS and CPDAI were 1.9 and 2, respectively. CONCLUSION: An MDA score of 7/7 is proposed as very low disease activity in psoriatic arthritis. Using this definition, the equivalent cutoffs for PASDAS and CPDAI are 1.9 and 2, respectively.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".