Stricter treat-to-target in RA does not result in less radiographic progression: a longitudinal analysis in RA BIODAM
Bibliographic record
Abstract
OBJECTIVES: To investigate whether meticulously following a treat-to-target (T2T)-strategy in daily clinical practice will lead to less radiographic progression in patients with active RA who start (new) DMARD-therapy. METHODS: Patients with RA from 10 countries starting/changing conventional synthetic or biologic DMARDs because of active RA, and in whom treatment intensification according to the T2T principle was pursued, were assessed for disease activity every 3 months for 2 years (RA-BIODAM cohort). The primary outcome was the change in Sharp-van der Heijde (SvdH) score, assessed every 6 months. Per 3-month interval DAS44-T2T could be followed zero, one or two times (in a total of two visits). The relation between T2T intensity and change in SvdH-score was modelled by generalized estimating equations. RESULTS: In total, 511 patients were included [mean (s.d.) age: 56 (13) years; 76% female]. Mean 2-year SvdH progression was 2.2 (4.1) units (median: 1 unit). A stricter application of T2T in a 3-month interval did not reduce progression in the same 6-month interval [parameter estimates (for yes vs no): +0.15 units (95% CI: -0.04, 0.33) for 2 vs 0 visits; and +0.08 units (-0.06; 0.22) for 1 vs 0 visits] nor did it reduce progression in the subsequent 6-month interval. CONCLUSIONS: In this daily practice cohort, following T2T principles more meticulously did not result in less radiographic progression than a somewhat more lenient attitude towards T2T. One possible interpretation of these results is that the intention to apply T2T already suffices and that a more stringent approach does not further improve outcome.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".