Increasing Rates of Arthroplasty for Psoriatic Arthritis in the United Kingdom Between 1995 and 2010
Bibliographic record
Abstract
Objective Arthroplasty requirements among patients with psoriatic arthritis (PsA) are not well known. This information is important to clinical and policy stakeholders for health‐system planning and may serve as a surrogate for estimation of the efficacy of disease‐modifying therapy. Methods We utilized The Health Improvement Network (THIN), a large general practice medical records database in the UK, to assess rates of primary total arthroplasty among patients with PsA and the general population between the years 1995 and 2010. Linear regression was used to estimate arthroplasty rates for the 2 cohorts during the study period, and Poisson regression was used to determine age‐ and sex‐adjusted incidence rate ratios (IRRs) between the PsA and general population cohorts. Results We identified 5,619 patients with incident PsA and 5,090,814 eligible patients from the general population between 1995 and 2010. In total, 187 primary total arthroplasties were documented in patients with PsA, and 80,163 primary total arthroplasties were documented in the general population. A trend of increasing arthroplasty rates was observed for both the PsA (R2 = 0.809; P < 0.0001) and general population (R2 = 0.890; P < 0.0001) cohorts during the study period. After adjustment for age and sex, patients with PsA had a first arthroplasty incidence rate that was twice that of the general population (IRR 2.01 [95% confidence interval 1.73–2.34]; P < 0.0001), notably beyond the year 2003 when biologic therapies were introduced. Conclusion Both the general population and patients with PsA have experienced increasing rates of first arthroplasty from 1995 to 2010, although the overall incidence rate was significantly higher for those with PsA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".