Tight Control and Radiological Progression: The Radiographic Outcomes of the TICOPA Study
Bibliographic record
Abstract
OBJECTIVE: The Tight Control of Psoriatic Arthritis (TICOPA) study was the first to undertake the treat-to-target approach in psoriatic arthritis (PsA). Our aim was to further investigate the radiographic changes in the TICOPA study. METHODS: The TICOPA trial recruited patients with early treatment-naïve PsA. Plain radiographs of the hands and feet were taken at weeks 0 and 48. Clinical outcomes were recorded by a blinded assessor every 12 weeks. In post hoc analysis, bootstrapped quantile regression, adjusting for baseline values and minimization factors, was used to compare radiographic scores (modified Sharp/van der Heijde [mSvdH]), defined according to treatment arm or disease activity states. RESULTS: Paired baseline and week 48 radiographs were available for 169/206 (82%) at week 48 (84 tight control [TC] arm, 85 standard care [StdC] arm). There was no difference in change in total mSvdH score seen with TC compared to StdC (median [IQR] 0.0 [-2.0 to 0.5] vs 0.0 [-2.0 to 0.0]; difference 0.0 [95% CI 0.0-0.0]). Median total mSvdH score change was lower in those achieving minimal disease activity, Disease Activity in Psoriatic Arthritis remission, and very low disease activity. The number of people with radiographic progression (an increase in total erosion score of ≥ 2 at week 48) was numerically lower in the TC group (5/84 [5.9%] vs 12/85 [14.1%]). Patients with radiographical progression presented with polyarticular disease and high C-reactive protein, and had poorer clinical outcomes at weeks 12 and 24. CONCLUSION: These data confirm the benefit of achieving low disease activity states on subsequent radiographic outcomes but did not show a significant impact related to a TC management approach.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".