Relation of NSAIDs, DMARDs, and TNF Inhibitors for Ankylosing Spondylitis and Psoriatic Arthritis to Risk of Total Hip and Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Ankylosing spondylitis (AS) and psoriatic arthritis (PsA) often affect the hip and/or knee. If effective, treatments might reduce risk of total hip or total knee arthroplasty (THA/TKA). We evaluated risk of THA/TKA related to use of medical therapies in AS/PsA. METHODS: We conducted a nested case-control study using 1994-2018 data from the OptumLabs Data Warehouse, which includes deidentified medical and pharmacy claims, laboratory results, and enrollment records for commercial and Medicare Advantage enrollees. Among those with AS/PsA, THA/TKA cases were matched up to 4 controls by sex, age, AS/PsA diagnosis, diagnosis year, insurance type, obesity, and prior THA/TKA. We assessed AS/PsA treatment 6 months prior to THA/TKA, including disease-modifying antirheumatic drugs (DMARDs) and tumor necrosis factor inhibitors (TNFi), alone or in combination, stratified by nonsteroidal antiinflammatory drug (NSAID) use. We evaluated the relation of treatment to risk of THA/TKA using conditional logistical regression with adjustment for confounders. RESULTS: Among 16,748 adults with AS, there were 444 THA/TKA cases and 1613 matched controls. Among 34,512 adults with PsA, there were 1003 cases and 3793 controls. Adjusted ORs for treatment category and THA/TKA ranged from 0.60 to 1.92; however, none were statistically significant. Results were similarly null in several sensitivity analyses. CONCLUSION: Odds of THA/TKA were not reduced with any combinations of NSAIDs, DMARDs, or TNFi among persons with AS or PsA. Given current utilization patterns in this population of US adults with AS and PsA, these medical therapies did not appear to be associated with less end-stage peripheral joint damage.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".