Higher risk of incident ankylosing spondylitis in patients with uveitis: a secondary cohort analysis of a nationwide, population-based health claims database
Bibliographic record
Abstract
OBJECTIVES: Ankylosing spondylitis (AS) is a progressive, systemic, inflammatory autoimmune disease that typically affects young adults. Uveitis is a common extra-articular manifestation of AS. Nevertheless, the magnitude of the risk of AS among patients with uveitis is not clear. The aim of this secondary retrospective cohort study was to investigate the risk of incident AS in patients with uveitis using data from a nationwide, population-based health claims research database. METHOD: Using Taiwan's National Health Insurance Research Database, we identified 6637 patients with uveitis between 2000 and 2012. A comparison cohort was assembled, which consisted of five patients without uveitis, based on frequency matching for gender, 10 year age interval, and index year, for each patient with uveitis. Both groups were followed until diagnosis of AS or the end of the follow-up period. A Poisson regression model was used to calculate the incidence rate ratio for AS between the uveitis cohort and the comparison cohort. RESULTS: Patients with uveitis exhibited a significantly higher incidence of AS than the comparison cohort (adjusted incidence rate ratio = 2.57, p < 0.001). Subgroup analysis with stratification by the interval between the diagnosis of uveitis and AS indicated that the adjusted incidence rates were significantly higher in the uveitis cohort with an interval of up to 7.9 years. CONCLUSION: A significant increased risk in AS among patients with uveitis was observed, with a time lag of up to 7.9 years between the diagnosis of uveitis and subsequent diagnosis of AS.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".