How Important Are the Standard Measures in Assessing the Clinical Outcomes in Psoriatic Arthritis in Real-world Settings?
Bibliographic record
Abstract
Over the last few years, the worldwide interest in psoriatic arthritis (PsA) has progressively increased because of better comprehension of the pathogenetic pathways that underlie the disease, as well as the ensuing availability on the market of drugs against specific molecular targets. Nevertheless, the selective blocking of a specific target in vivo has provided evidence that the role of a specific pathologic pathway is not absolute and that patients with PsA may benefit from different biological drugs. This suggests that the target to be inhibited varies among patients, and not infrequently within the same patient during the course of the disease. This scenario is consistent with the clinical heterogeneity of PsA and accounts for the compelling need to define clear-cut clinical phenotypes to be treated with the appropriate drug. As a consequence, unlike rheumatoid arthritis (RA), whose therapeutic algorithm is agreed upon all over the world, the best therapeutic strategy to adopt in PsA is still a matter of debate, as the different approaches advocated by the international recommendations from the European League Against Rheumatism and the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis demonstrate1. In addition, the complexity of the disease makes PsA disease activity rather difficult to assess: as a result, we still are missing a comprehensive composite score capable of estimating the different clinical manifestations of PsA, and more importantly, the clinical outcomes of our treatments. The latter is maybe the most compelling and critical issue in the field of PsA together with the still-open debate about which measures should be carried out in selecting the eligible patients to commence a biologic drug and in evaluating the clinical outcomes. This matter is definitely far more troublesome in real-world settings. However, … Address correspondence to Dr. F. Iannone, Rheumatology Unit, Policlinico, Piazza G. Cesare 11, 70124 Bari, Italy. E-mail: florenzo.iannone{at}uniba.it
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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 | 0.232 | 0.409 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".