Profiling Response to Tumor Necrosis Factor Inhibitor Treatment in Axial Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: Lack of response to tumor necrosis factor inhibitor (TNFi) agents is not uncommon, encountered during the treatment of axial spondyloarthritis (SpA) patients, and it can be classified as primary lack of response (PLR) or secondary lack of response (SLR). The primary aim of this study was to evaluate factors associated with TNFi failure types and their characteristics in axial SpA. METHODS: Adult axial SpA patients who were TNFi naive at the time of baseline evaluation and started receiving their first biologics for active axial disease were identified. Based on the clinical response to the first TNFi, patients were then stratified into 3 groups: PLR, SLR, and responders. Clinical, demographic, and laboratory data were collected and analyzed. RESULTS: There was a total of 249 axial SpA patients in the study (70.7% male, mean ± SD age 37.3 ± 12.4 years), which included PLR (n = 62), SLR (n = 93), and responders (n = 94). PLR patients tended to be older, with a lower HLA-B27 rate, a higher percentage of nonresponder axial SpA patients, and a higher baseline Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score compared to SLR patients or responders. In multiple regression analysis, increasing age, negative HLA-B27, higher baseline BASDAI, and treatment with the soluble TNF receptor protein were the independent predictors of PLR. CONCLUSION: PLR accounted for nearly 40% of the TNFi failures in axial SpA patients. Older age, negative HLA-B27, higher baseline disease activity, and treatment with soluble TNF receptors were the independent predictors of the primary nonresponse to TNFi.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".