Bibliographic record
Abstract
Psoriatic arthritis (PsA) has many disease manifestations leading to diverse patient phenotypes. Treatment responses in the same individual may diverge for concomitantly active PsA components. For this reason, evaluation of disease activity and treatment effectiveness for PsA should ideally include assessment of the complete spectrum of manifestations: psoriasis of skin and nails, arthritis, enthesitis, dactylitis, axial spondyloarthritis (SpA), systemic inflammation, and effect on life. Signs of active disease could then be systematically addressed for each individual. Comprehensive PsA assessment is often challenging in clinical practice because of limitations on time and resources for rheumatology clinic visits. However, clinicians are uniformly confronted with effectiveness questions when treating PsA: Is the treatment working? Is it time to switch therapies? Which treatment should be next? Cañete, et al , as described in this issue of The Journal 1, conducted a consensus exercise to define the effectiveness of biological disease-modifying antirheumatic drugs (bDMARD) and to support PsA treatment continuation decisions in clinical care. The study addresses a few scenarios relevant to clinicians such as disease severity or prior damage, peripheral and axial disease, and prior biologic experience. Criteria for bDMARD continuation are met in both peripheral and axial PsA if low disease activity (LDA) state and meaningful improvement [score improvement ≥ 3 in Psoriatic Arthritis Impact of Disease (PsAID)]2 have concomitantly been achieved (Table 1). The alternative continuation criteria, however, allow high or moderate disease activity in patients with severe PsA/damage/multiple biologic failures, as long as a major response to therapy and a patient acceptable symptom state in the PsAID (score ≤ 4) have concomitantly been achieved (Table 1). Because these effectiveness criteria define the lowest threshold for continuation of the treatment in the course in clinical care, they may shape PsA treatment and progression in the future. Comparing treatment strategies becomes … Address correspondence to Dr. A.M. Orbai, Johns Hopkins University School of Medicine, Division of Rheumatology, 5200 Eastern Ave., MFL Center Tower, Suite 4100, Baltimore, MD 21224, USA. Email: aorbai1{at}jhmi.edu.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".