What Should Be the Primary Target of “Treat to Target” in Psoriatic Arthritis?
Bibliographic record
Abstract
OBJECTIVE: Recommendations regarding "treat to target" in psoriatic arthritis (PsA) have stated that the target should be remission or inactive disease. Potential definitions include very low disease activity (VLDA), PsA Disease Activity Score (PASDAS) near remission, Disease Activity Index for PsA (DAPSA) or clinical DAPSA (cDAPSA) remission. Our aim was to investigate the proportion of patients who fulfill these definitions and how much residual active disease remained. METHODS: This analysis used 2 datasets: first, trial data from the Tight Control of PsA (TICOPA) study, which included 206 patients with recent-onset (< 2 yrs) PsA receiving standard and biological disease-modifying antirheumatic drugs (DMARD); and second, an observational clinical dataset from Italy of patients receiving biological DMARD. Proportions achieving each of the 4 potential targets were calculated in each dataset and comparisons between treatment groups were performed in the TICOPA dataset. Levels of residual disease were established for key clinical domains of PsA. RESULTS: All measures could differentiate the TICOPA trial treatment groups (p < 0.03). Lower proportions of patients fulfilled the VLDA criteria compared to DAPSA or cDAPSA remission. PASDAS results were different between the cohorts. Residual active disease was low across all definitions although higher levels were seen in DAPSA and cDAPSA compared to VLDA, particularly for psoriasis. In all measures, the proportion with elevated C-reactive protein was similar and low. CONCLUSION: VLDA appears the most stringent measure. It ensures that significant active arthritis, enthesitis, and psoriasis are not present, in contrast with DAPSA and PASDAS, in which composite scores can "hide" active disease in some domains.
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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 | 0.029 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".