Evaluating functional recovery post-total knee replacement, a WOMAC based comparison of synovectomy versus non-synovectomy approaches in a tertiary care hospital in South India
Bibliographic record
Abstract
Background: Synovectomy is commonly performed during total knee arthroplasty (TKA) for patients with osteoarthritis (OA) and inflammatory arthritis to address synovial inflammation. However, its impact on long-term functional outcomes remains unclear. Objective of this study was to evaluate the effect of synovectomy during TKA on postoperative pain, stiffness, and functional outcomes in patients with OA and inflammatory arthritis. Methods: A hospital-based retrospective cohort study was conducted with 86 patients who underwent TKA one year prior (43 with synovectomy, 43 without). The Western Ontario and McMaster osteoarthritis index (WOMAC) was used to assess pain, stiffness, and physical function at follow-up. Results: The mean age of patients was 65.4 years. No significant differences were found in pain (p=0.638) and stiffness (p=0.647) between the two groups. However, physical function was significantly better in the group without synovectomy (p=0.004), with significantly fewer difficulties in activities such as standing, bending, and getting in/out of bed (p<0.05). The mean total WOMAC score was significantly lower in the group without synovectomy (p=0.045). Conclusions: Synovectomy during TKA had no impact on postoperative pain or stiffness and was associated with poorer functional outcomes compared to TKA without synovectomy. These findings suggest that synovectomy may not offer additional benefits for patients with OA and inflammatory arthritis. Further research with larger samples is recommended to better understand its role in TKA.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".