Meta-analysis of the efficacy of lateral unicompartmental knee arthroplasty and total knee arthroplasty in the treatment of isolated lateral compartment knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To systematically evaluate the efficacy of lateral unicompartmental knee arthroplasty (LUKA) and total knee arthroplasty (TKA) in the treatment of isolated lateral compartment knee osteoarthritis (LCKO), and to provide guidance and a basis for selecting surgery in clinical practice. METHODS: Inclusion and exclusion criteria for literature were established, appropriate effect indicators were selected, and PubMed, Web of Science, Embase, Medline, Cochrane Library, and CNKI databases were searched using a computer. The Newcastle Ottawa scale (NOS) was used to evaluate the quality of the literature. After data extraction, Meta-analysis was performed using Revman5.4 software. RESULTS: A total of 8 studies were included in this meta-analysis, involving 490 patients, with 204 in the TKA group and 286 in the LUKA group. Meta-analysis found that, there was no significant statistical difference in operation time between the LUKA group and the TKA group. Nevertheless, LUKA offers advantages such as reduced intraoperative blood loss and shorter hospitalization time. At the same time, the LUKA group also had advantages over the TKA group in postoperative visual analogue scale (VAS) score, knee joint range of motion, Oxford Knee Score (OKS)score and Hospital for Special Surgery (HSS) score. CONCLUSION: When LUKA and TKA are used to treat isolated LCKO, LUKA has the advantages of less intraoperative blood loss, shorter hospital stay, lower postoperative VAS score, and better knee function score and range of motion.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.057 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.003 | 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".