Patterns and Associated Risk of Perioperative Use of Anti-Tumor Necrosis Factor in Patients with Rheumatoid Arthritis Undergoing Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVE: The patterns and risks of perioperative use of anti-tumor necrosis factor (anti-TNF) medication in patients with rheumatoid arthritis (RA) are not well studied. We examined the patterns of perioperative anti-TNF use and risk of postoperative adverse events (AE) in patients undergoing total knee replacement (TKR). METHOD: Retrospective cohort study with followup. RA cases within a TKR registry were identified by ICD-9 code (714.0) or self-report. Mailed questionnaires queried anti-TNF use and duration of RA. AE were determined by chart review and patient self-report, and included surgical site infection, pulmonary embolus, deep venous thrombosis, pneumonia, and any infection or re-operation within 6 months. RESULTS: There were 268 TKR cases with RA. The stop time for anti-TNF preoperatively correlated with dosing schedule; restart time was after wound healing. There were 7 surgical site infections (3%), one (0.4%) of which was a deep joint infection in bilateral TKA requiring explant. The anti-TNF group had 3.26% (3/92) local site infection versus 2.10% (3/143) in the group without anti-TNF and this difference was not statistically significant (Fisher exact test, p = 0.68). The one deep joint infection was in the anti-TNF group. Six-month AE rate was 7.61% in the anti-TNF group versus 6.99% in the group without anti-TNF (Fisher exact test, p = 1.0). CONCLUSION: There was a low risk of infection and perioperative adverse events in patients with RA receiving anti-TNF therapy who were undergoing TKR. This raises the question whether it is necessary to stop anti-TNF for a long period prior to surgery. Given the possible risks associated with stopping anti-TNF, including worsening of disease, further study is needed to determine optimal perioperative use of anti-TNF among patients with RA undergoing TKR.
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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 | 0.003 |
| 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) | 0.001 | 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".