Rheumatoid Arthritis Does Not Increase Risk of Short-term Adverse Events after Total Knee Arthroplasty: A Retrospective Case-control Study
Bibliographic record
Abstract
OBJECTIVE: More adverse events (AE) are reported after total knee arthroplasty (TKA) for patients with rheumatoid arthritis (RA) than for patients with osteoarthritis (OA). This study evaluates 6-month postoperative AE in a high-volume center in a contemporary RA cohort. METHODS: Patients with RA in an institutional registry (2007-2010) were studied. AE were identified by self-report and review of office and hospital charts. Subjects with RA were matched to 2 with OA by age, sex, and procedure. RA-specific surgical volume was determined. Baseline characteristics and AE were compared and analyzed. RESULTS: There were 159 RA TKA and 318 OA. Of the patients with RA, 88.0% were women, 24.5% received corticosteroids, 41.5% received biologics, and 67% received nonbiologic disease-modifying antirheumatic drugs (DMARD). There was no difference in comorbidities. RA-specific surgical volume was high; 64% of cases were performed by surgeons with ≥ 20 RA cases during the study period. Patients with RA had worse baseline pain and function and lower perceived health status (EQ-5D 0.59 vs 0.65, p < 0.01). There were no deep infections in either group and no difference in superficial infection (9.4% RA vs 10.1% OA, p = 0.82), myocardial infarction (0.7% RA vs 0% OA, p = 0.33), or thromboembolism (1.3% RA vs 0.6% OA, p = 0.60). CONCLUSION: In a high-volume center, with high RA-specific experience, RA does not increase postoperative AE. Despite worse preoperative function and high steroid and DMARD use, complications were not increased. However, further study to determine generalizability is needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".