Incident osteoarthritis and osteoarthritis-related joint replacement surgery in patients with ankylosing spondylitis: A secondary cohort analysis of a nationwide, population-based health claims database
Bibliographic record
Abstract
BACKGROUND: Ankylosing spondylitis (AS) might be associated with an increased risk of secondary osteoarthritis. However, there is a lack of studies assessing its impact on osteoarthritis-related surgery. The aim of this secondary cohort study was to investigate the risk of symptomatic osteoarthritis and osteoarthritis-related surgery, including total hip replacement surgery (THRS) and total knee replacement surgery (TKRS) in patients with AS. METHODS: Using the Taiwan's National Health Insurance Research Database, we identified 3,462 patients with AS between 2000 and 2012. A comparison cohort was assembled consisting of five patients without AS, based on frequency matching for sex, 10-year age interval, and index year, for each patient with AS. Both groups were followed until diagnosis of the study outcomes or the end of the follow-up period. RESULTS: Male patients with AS exhibited a significantly higher incidence of osteoarthritis (adjusted incidence rate ratio [IRR] 1.43; P < 0.001), THRS (adjusted IRR 12.59; P < 0.001), and TKRS (adjusted IRR 1.89; P = 0.036). Moreover, analyses stratified by age group (20-39 years versus 40-80 years) indicated a high IRR (adjusted IRR 27.66; P <0.001) for THRS among younger patients with AS. CONCLUSIONS: Male patients with AS had a significant higher risk of developing osteoarthritis, and receiving THRS and TKRS. Young patients with AS also showed a significant higher risk of receiving THRS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".