Tumour necrosis factor inhibitor therapy and infection risk in axial spondyloarthritis: results from a longitudinal observational cohort
Bibliographic record
Abstract
OBJECTIVES: Long-term data on infection risk in axial SpA (axSpA) are sparse. TNF inhibitors (TNFis) are increasingly being used in axSpA, with infection being the most important adverse event. We aimed to investigate the frequency of infections in axSpA and to identify factors predisposing to infection. METHODS: Data were extracted from a longitudinal observational cohort of patients with axSpA. Infection rates were calculated and multivariate analysis was performed to investigate the association of independent variables with infection. RESULTS: Data were analysed for 440 patients followed for a total of 1712 patient-years (pys). A total of 259 infections, of which 23 were serious, were recorded in 185 patients. The overall rate of any infection was 15 (95% CI 13, 17)/100 pys and the serious infection rate was 1.3 (95% CI 0.9, 2.0)/100 pys. There was no significant difference in the rate of any infection or serious infection in patients on TNFis compared with patients never on biologic agents. In the multivariate analysis, DMARD treatment, but not TNFi treatment, was associated with risk of infection. Age, disease duration, smoking status, BASFI, BASDAI, co-morbidity score and hospitalization were not associated with an increased risk of infection. CONCLUSION: The serious infection rate in axSpA in this observational cohort is low when compared with rates reported in other rheumatic diseases. Biologic use was not a significant risk factor for serious infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".