Difficult-to-Treat Concept in Psoriatic Arthritis: Analysis of 2 Potential Definitions in a Large Group of Patients. A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The main aim of the study was to evaluate the performance of 2 proposed criteria for difficult-to-treat (D2T) psoriatic arthritis (PsA) in a group of patients and to evaluate the agreement between the 2 sets of criteria. METHODS: We performed a cross-sectional analysis of 2 longitudinal cohorts of patients with PsA fulfilling the Classification Criteria for Psoriatic Arthritis (CASPAR), with at least 1 year of follow-up. A detailed medical history was collected and a physical examination was performed for all recruited patients. The proposed criteria for patients with D2T PsA were applied in our group. To test the performance of the 2 sets of criteria, we used an external validator (absence of patient acceptable symptom state + physician global assessment ≥ 6 cm). Finally, the agreement between the 2 sets of criteria was assessed. RESULTS: We evaluated 378 patients with PsA (219 male/159 female), with a mean age (range) of 58 (19-75) years. Seventy-five (19.8%) patients fulfilled the D2T criteria proposed by Perrotta et al and 58 (15.3%) the D2T criteria proposed by Kumthekar et al. Both criteria showed comparable performance, with low sensitivity (Perrotta: 37.8%, Kumthekar: 29.7%) but good specificity (Perrotta: 82.1%, Kumthekar: 86.2%). Finally, the agreement between the 2 sets of criteria is substantial (Fleiss [Formula: see text] 0.72), suggesting that both criteria identify nearly the same group of patients. CONCLUSION: Our study compared 2 published sets of criteria showing comparable performance and substantial agreement. This study may pave the way for further research in this field.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".