Factors Associated With Treatment Pathways in Early Axial Spondyloarthritis: A Multistate Analysis of the 10-Year Follow-Up of the DESIR Cohort
Bibliographic record
Abstract
OBJECTIVE: Current recommendations for the management of patients with axial spondyloarthritis (axSpA) emphasize the need of an individualized strategy in therapeutic decision-making. The study objectives were to describe therapeutic strategies observed in axSpA, and to assess the factors associated with treatment intensification over time. METHODS: ), with a scheduled 10-year follow-up. A multistate model with 4 ordered treatment states (no treatment, nonsteroidal antiinflammatory drugs [NSAIDs], conventional synthetic disease-modifying antirheumatic drugs [csDMARDs], and tumor necrosis factor inhibitors [TNFi]) was defined, with 6 possible transitions. Restricted mean sojourn times in each state were estimated. Then, predictors of those transitions were assessed by multivariable Cox models. RESULTS: A total of 686/708 (96.9%) patients who had > 1 visit were analyzed. At cohort entry, 199 (29%) were untreated, 427 (62.2%) were receiving NSAIDs, 60 (8.7%) csDMARDs, and none were receiving TNFi. Over the follow-up period, patients mostly (46.4% of the time) received NSAIDs, followed by TNFi (24.4% of the time). The presence of sacroiliitis on radiographs, inflammatory bowel disease, and articular index were jointly associated with the transition to NSAIDs. Longer duration in the previous state often decreased the hazard of the transition to csDMARDs or TNFi. Worse disease activity outcomes increased the hazard of most transitions. CONCLUSION: To our knowledge, this was the first study using a multistate model to easily represent different treatment states, detailing the transitions across them and their associated factors. Different time profiles for the management of patients with axSpA were identified, including in those abstaining from treatment up to a significant proportion of patients treated with csDMARDs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".