Musculoskeletal Surgery in Psoriatic Arthritis: Prevalence and Risk Factors
Bibliographic record
Abstract
Objective Despite medical therapy, damage occurs in patients with psoriatic arthritis (PsA) requiring musculoskeletal (MSK) surgery. We aimed to describe MSK surgery in patients with PsA and identify risk factors for undergoing first MSK surgery attributable to PsA. Methods A single-center cohort identified patients with PsA fulfilling Classification Criteria for Psoriatic Arthritis who had MSK surgery between January 1978 and December 2019 inclusive. Charts were reviewed to confirm surgeries were MSK-related and attributable to PsA. Descriptive statistics determined MSK surgery prevalence and types. Cox proportional hazards models evaluated clinical variables for undergoing first MSK surgery using time-dependent covariates. Using a dataset with 1-to-1 matching on markers of PsA disease severity, a Cox proportional hazards model evaluated the effect of targeted therapies, namely biologics on time to first MSK surgery. Results Of 1574 patients, 185 patients had 379 MSK surgeries related to PsA. The total number of damaged joints (hazard ratio [HR] 1.03,P< 0.001), tender/swollen joints (HR 1.04,P= 0.01), presence of nail lesions (HR 2.08,P< 0.01), higher Health Assessment Questionnaire scores (HR 2.01,P< 0.001), elevated erythrocyte sedimentation rate (HR 2.37,P= 0.02), and HLA-B27 positivity (HR 2.22,P= 0.048) were associated with increased risk of surgery, whereas higher Psoriasis Area Severity Index (HR 0.88,P< 0.002) conferred a protective effect in a multivariate model. The effect of biologics did not reach statistical significance. Conclusion MSK surgery attributable to PsA is not rare, affecting 11.8% of patients. Markers of cumulative disease activity and damage are associated with a greater risk of requiring surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".