Better clinical outcome of total knee arthroplasty for rheumatoid arthritis with glucocorticoids and disease-modifying anti-rheumatic drugs after an average of 11.4-year follow-up
Bibliographic record
Abstract
Objectives: This study investigated whether perioperative treatment with glucocorticoids (GC) and disease-modifying anti-rheumatic drugs (DMARDs) can improve clinical outcomes and reduce long-term complications for patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). Methods: Patients were allocated into three groups based on perioperative drug therapy: A. control group (no GC or DMARDs), B. DMARD group (DMARDs given without GC) and C. co-therapy group (DMARDs plus GC). The patients were followed and received questionnaires at the latest follow-up. Baseline characteristics, pre- and post-operative HSS knee score, laboratory parameters, and surgical complications were collected and analyzed. Results: 56 RA patients undergoing 91 TKAs were included in this study. The average follow-up duration was 11.4 years. Patients who received perioperative GC with DMARDs (group C) achieved better HSS score (C:84.04 vs A:78.96 vs B:76.50, p=.008), pain relief (VAS: C: 1.12 vs B: 1.73, p=0.02), higher functional assessment (C:16.17 vs B:13.23, p=0.03) and range of motion (C:132.15 vs A:112.57 vs B:112.51, p<0.001) compared the other treatment groups at time of latest follow-up. Aside from greater post-operative hemoglobin seen in group A compared to group B (P=0.04), no other differences were noted in laboratory tests, blood loss and transfusion, short-term or long-term complications between treatment groups. Conclusions: Perioperative treatment with GC combined DMARDs for RA patients is associated with improved HSS score, better function and range of motion, and reduced postoperative pain in the long term when compared to treatment with DMARDs alone or management without anti-rheumatic medication.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".