The Assessment of Disease Activity in Psoriatic Arthritis: MDA, VLDA, DAPSA, or Something Else?
Bibliographic record
Abstract
Psoriatic arthritis (PsA) is a multifaceted disease. Within this definition, rheumatologists can every day face various aspects of the disease and try to deal with them by choosing the right treatment. This could be a very easy task when some phenotypic manifestations and detectable, objective disease activity are present, but sometimes the same disease could be very challenging when some manifestations such as pain, fatigue, or even enthesitis are predominant and associated with less detectable disease activity. Therefore, in the last decade, a significant effort has been made to identify potential instruments for the assessment of the disease activity as a “whole” and as the main target to be treated. In fact, rheumatologists have raised the bar, moving (as target to treat) from the achievement of good control of single domains such as pain, function, inflammation, skin, and quality of life to a more comprehensive disease control of PsA, aimed at managing the disease in all its components. Indeed, the idea of developing a potential instrument to measure all disease domains was absolutely remarkable, and various ones were eventually developed and validated. Minimal disease activity (MDA)1, very low disease activity (VLDA)2, and the Disease … Address correspondence to Dr. E. Lubrano, University of Molise, Academic Rheumatology Unit, Department of Medicine and Health Sciences, Via Giovanni Paolo II, 86100 Campobasso, Italy. E-mail: enniolubrano{at}hotmail.com
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".