Incidence and predictors of demyelinating disease in spondyloarthritis: data from a longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to investigate the incidence of demyelinating disease (DD) among SpA patients and to identify risk factors that predict DD in this patient population. METHODS: Axial SpA (axSpA) and PsA patients were identified from a longitudinal cohort database. Each group was analysed according to the presence or absence of DD. Incidence rates (IRs) of DD were obtained, with competing risk analysis. Cox regression analysis (with Fine and Gray's method) was used to evaluate predictors of DD development. RESULTS: Among 2260 patients with follow-up data, we identified 18 DD events, corresponding to an average IR of 31 per 100 000 persons per year for SpA. The IR of DD at 20 years was higher in axSpA than in PsA (1.30% vs 0.13%, P = 0.01). The risk factors retained in the best predictive model for DD development included ever- (vs never-) smoking [hazard ratio (HR) 2.918, 95% CI 1.037-8.214, P = 0.0426], axSpA (vs PsA) (HR 8.790, 95% CI 1.242-62.182, P = 0.0294) and presence (vs absence) of IBD (HR 5.698, 95% CI 2.083-15.589, P = 0.0007). History of TNF-α inhibitor therapy was not a predictor of DD. CONCLUSION: The overall incidence of DD in this SpA cohort was low. Incident DD was higher in axSpA than in PsA. A diagnosis of axSpA, the presence of IBD, and ever-smoking predicted the development of DD. History of TNF-α inhibitor use was not found to be a predictor of DD in this cohort.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".