Sustained low functional impairment in axial spondyloarthritis (axSpA): which are the primary outcomes that should be targeted to achieve this?
Bibliographic record
Abstract
Abstract Objectives To (i) determine whether sustained disease activity states, as measured by Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Ankylosing Spondylitis Disease Activity Score (ASDAS), impact function, and (ii) evaluate characteristics predicting sustained low functional impairment in a prospective axial spondyloarthritis (axSpA) cohort. Methods Biologic Treatment Registry Across Canada (BioTRAC) was a multi-center, prospective registry that collected real-world data on axSpA patients receiving infliximab or golimumab between 2006 and 2017. Generalized estimating equations (GEE) were used to test baseline characteristics, treatment, and duration (at 6 and 12 months vs. only at 6 or 12 months vs. neither) of low BASDAI (< 3), ASDAS-inactive disease (ID)(< 1.3), and ASDAS-low disease activity (LDA) in predicting sustained low Bath Ankylosing Spondylitis Functional Index (BASFI)(< 3) between 12 and 18 months. The adjusted impact of achieving low disease state at 6 and/or 12 months on BASFI at 18 months was analyzed by generalized linear models. Results Eight hundred ten patients were enrolled. 33.7%, 13.4%, and 24.7% achieved sustained low BASDAI, ASDAS-ID, and ASDAS-LDA, respectively. In univariable GEE of baseline variables, age and baseline BASDAI, BASFI, and ASDAS significantly predicted sustained low BASFI. In multivariable GEE, sustained low BASDAI (p < 0.001), low BASDAI only at 6 or 12 months (p = 0.001), and baseline BASFI (p < 0.001) were the only predictors of sustained low BASFI. Sustained ASDAS-ID (p = 0.040) and ASDAS-LDA (p < 0.001) were also predictors when forced into the model. Similar results were obtained when evaluating the BASFI score at 18 months. Conclusion Sustained BASDAI < 3 may be a valid and feasible target for a treat-to-target strategy in axSpA having function as treatment goal.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".