Function scores of different surgeries in the treatment of knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is the third most common diagnosis made by general practitioners in older patients. The aim of this study was to compare the function scores of different surgeries in the treatment of knee osteoarthritis (KOA). METHODS: Cohort studies about different surgical treatments for KOA were included with a comprehensive search in PubMed, Cochrane Library, and Embase. The standard mean difference (SMD) value was evaluated and the surface under the cumulative ranking (SUCRA) curve was drawn with a combination of direct and indirect evidence. A total of 265 eligible patients were enrolled and served as the nonoperative treatment group, osteotomy group, unicompartmental knee arthroplasty (UKA) group, total knee arthroplasty (TKA) group, and arthroscopic surgery group. Before surgery, 6 months after surgery, 1 year after surgery and 5 years after surgery, the hospital for special surgery (HSS) knee score, Lysholm score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, and American knee society score (KSS) were recorded. RESULTS: A total of 9 cohort studies including 954 patients with KOA were finally enrolled into the study. The network-meta analysis revealed that osteotomy and UKA treatments showed a better efficacy on improving the function score. Our cohort study further confirmed that, a higher HSS knee score after 1 year and higher Lysholm score after 6 months and 1 year were observed in the osteotomy and UKA groups, while better HSS knee score and KSS after 6 months and 1 year were showed in the osteotomy and TKA groups. In the TKA group, Lysholm score and KSS were higher and WOMAC score was lower after 5 years than other groups. WOMAC score was lowest in the UKA group after 6 months, 1 year and 5 years of surgery. CONCLUSION: These results provide evidence that function scores of patients with KOA were improved by osteotomy, UKA, TKA, and arthroscopic surgery. And osteotomy and UKA showed better short-term efficacy, while TKA appeared better long-term efficacy.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".