Unicompartmental knee arthroplasty versus high tibial osteotomy for medial knee osteoarthritis: A systematic review and meta-analysis
Bibliographic record
Abstract
We aimed to systematically compare the clinical and functional outcomes between unicompartmental knee arthroplasty (UKA) and high tibial osteotomy (HTO) for the treatment of medial knee osteoarthritis (KOA). Literatures were searched from PubMed, EMBASE, the Cochrane library, Wanfang DATA, China National Knowledge Infrastructure (CNKI) and SinoMed database until December 2020. Studies comparing postoperative clinical and functional outcomes of UKA versus HTO were included. Totally, 38 studies were included, including 2368 patients with 2393 knees in HTO group and 6536 patients with 6571 knees in UKA group. There was significant difference in postoperative pain, revision rate, complications, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score between HTO and UKA groups ( p < 0.05). No significant difference was found in excellent/good surgical results, Lysholm, Hospital for Special Surgery (HSS) score, Knee Society Knee (KSS) score, knee and function score of Knee Society (KSFS) score and Tegner score between these two groups ( p > 0.05). UKA produced less postoperative pain, less complications and superior WOMAC score, whereas HTO offered extended range of motion (ROM) and less revision rate.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".