A new joint reconstruction technique in the treatment of giant cell tumors around the knee: Structural allograft and unicompartmental arthroplasty
Bibliographic record
Abstract
OBJECTIVE: The long-term prognosis of patients who underwent unicompartmental knee arthroplasty (UKA) with a structural allograft or hemiarticular allograft transplantation to treat giant cell tumors (GCTs) around the knee and the prosthesis survival rate were analyzed. METHODS: We retrospectively reviewed 73 patients who were diagnosed with GCTs around the knee and underwent surgery to restore joint function from 2000 to 2015. Patients were divided into two groups according to the surgical procedure used for functional knee reconstruction: hemiarticular allograft transplantation or structural allograft and UKA. The Knee Society Score (KSS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) were used to analyze postoperative knee function between the two groups. The Kellgren-Lawrence (K-L) classification system was used to evaluate the progression of osteoarthritis. The incidence of complications and the prosthesis survival rate were also investigated. RESULTS: Patients who underwent UKA to treat GCT demonstrated significantly improved knee function. The rate of an excellent or good KSS was significantly different between the two groups (p = 0.041 at the 1-year follow-up, p = 0.033 at the last follow-up). The proportion of severe cases according to WOMAC in the two groups was also different (p = 0.030 at the 1-year follow-up, p = 0.021 at the last follow-up). According to the K-L grade of unaffected compartments, UKA better prevented the progression of osteoarthritis (p = 0.034). CONCLUSIONS: Patients with GCTs around the knee could benefit from UKA. In addition to providing better knee function and range of motion, UKA could also slow the progression of osteoarthritis in the knee joint. This new surgical method could meet the needs of patients wishing to preserve joint integrity and favorable joint function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".