Does prior arthroscopic procedure impact outcomes of knee arthroplasty? A systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Despite generally favorable outcomes following knee arthroscopy, a certain subset of patients inevitably develops progression of knee disease, necessitating subsequent total knee arthroplasty (TKA). Therefore, the evaluation of TKA outcomes following arthroscopy has emerged as a major area of research. The aim of the current review is to measure the impact of prior arthroscopy on functional and adverse outcomes following TKA. MATERIALS AND METHODS: Literature search was conducted in the databases including Medline, EMBASE, PubMed Central, ScienceDirect, Google Scholar and Cochrane library from inception until April 2021. Meta-analysis with random-effects model was conducted to calculate pooled odds ratio (OR) or standardized mean difference (SMD) with 95% confidence interval (CI) depending on the type of outcome. RESULTS: In total, 9 studies with 185,013 participants were included in the review. The majority of the studies were conducted in the USA and China. Almost all the studies had low quality as per Newcastle Ottawa (NO) scale. The pooled SMD for functional outcome was -0.19 [95%CI: -0.30 to -0.09], while the pooled OR for revision rate was 1.53 (95% CI: 1.21 to 1.92). In terms of postoperative complications, the pooled OR for stiffness was 1.55 (95% CI: 0.92-2.61), infection was 1.39 (95%CI: 1.17-1.67), aseptic loosening was 1.93 (95% CI: 1.19-3.11), VTE was 1.06 (95% CI: 0.83-1.35), and MUA was 1.33 (95% CI: 1.13-1.57) respectively. CONCLUSIONS: Prior arthroscopy has significant impact on the functional and adverse clinical outcomes following TKA. Surgeons need to develop a comprehensive intervention plan to manage these high-risk patients and reduce the rate of postoperative complications.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".