Risk of 30-day Readmission After Knee or Hip Replacement in Rheumatoid Arthritis and Osteoarthritis by Non-Medicare and Medicare Payer Status
Bibliographic record
Abstract
OBJECTIVE: To determine the indication and risk of 30-day rehospitalization after hip or knee replacement among patients with rheumatoid arthritis (RA) and osteoarthritis (OA) by Medicare and non-Medicare status. METHODS: Using the Nationwide Readmission Database (2010-2014), we defined an index hospitalization as an elective hospitalization with a principal procedure of total hip (THR) or knee replacement (TKR) among adults aged ≥ 18 years. Primary payer was categorized as Medicare or non-Medicare. Survey logistic regression provided the odds of 30-day rehospitalization in RA relative to OA. We calculated the rates for principal diagnoses leading to rehospitalization. RESULTS: Overall, 3.53% of 2,190,745 index hospitalization had a 30-day rehospitalization. Patients with RA had a higher adjusted risk of rehospitalization after TKR (OR 1.11, 95% CI 1.02-1.21) and THR (OR 1.39, 95% CI 1.19-1.62). Persons with RA and OA did not differ with respect to rates of infections, cardiac events, or postoperative complications leading to the rehospitalization. After TKR, RA patients with Medicare had a lower venous thromboembolism (VTE) risk (OR 0.58, 95% CI 0.58-0.88), whereas those with RA had a greater VTE risk (OR 2.41, 95% CI 1.04-5.57) after THR. CONCLUSION: Patients with RA had a higher 30-day rehospitalization risk than OA after TKR and THR regardless of payer type. While infections, postoperative complications, and cardiac events did not differ, there was a significant difference in VTE as the principal diagnosis of rehospitalization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".