Is high tibial osteotomy better than proximal fibula osteotomy for treating knee osteoarthritis? A protocol for a systematic review and meta-analysis of clinical controlled trials
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a common disease in the elderly, which seriously reduces the quality of life of patients and increases the social burden. proximal fibula osteotomy (PFO) and high tibial osteotomy (HTO) are effective methods to treat KOA. However, it is not entirely clear which method has the advantage. Therefore, we evaluated the efficacy and safety of HTO and PFO in the treatment of KOA. METHODS: Randomized controlled trials from online databases including PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journal Database, Wanfang Data and Chinese Biomedical Literature Database that compared the efficacy of HTO and PFO in the treatment of KOA were retrieved. The main outcomes included hospital for special surgery (HSS) knee scores, knee society knee scoring system (KSS) score, visual analog scale (VAS) knee pain scores, western ontario and McMaster universities osteoarthritis index score, operation time, intraoperative bleeding volume, hospitalization time, complications. The Cochrane risk of bias tool was used to assess methodological quality. RESULTS: The literature will provide a high-quality analysis of the current evidence supporting HTO for KOA based on various comprehensive assessments including HSS scores, KSS score, VAS scores, western Ontario and McMaster universities osteoarthritis index score, operation time, intraoperative bleeding volume, hospitalization time, and complications. CONCLUSION: This proposed systematic review will provide up-to-date evidence to assess the effect of HTO in the treatment for patients with KOA.
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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 | 0.084 | 0.116 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.030 | 0.045 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".