Autogenous structural bone graft reconstruction of ≥ 10-mm-deep uncontained medial proximal tibial defects in primary total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Management of uncontained medial proximal tibial defects during primary total knee arthroplasty (TKA) can be challenging, especially for defects ≥ 10 mm in depth. This study sought to assess the outcomes of autogenous structural bone grafts to address these defects. MATERIALS AND METHODS: In this prospective study, patients with uncontained medial proximal tibial defects ≥ 10 mm in depth undergoing TKA were managed by autogenous structural bone grafts fixed by screws and were followed up for at least 36 months. Patients were followed-up clinically with Knee Society Score (KSS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Additionally, radiological follow-up was done to assess bone graft union and implant stability. RESULTS: . The mean defect depth was 17 ± 3.6 mm. With a mean follow-up period of 52.2 ± 12.3 months, the median KSS improved significantly from 30 preoperatively to 89, P < 0.001. The median WOMAC score reduced significantly from 85 preoperatively to 30.5, P < 0.001. The mean ROM increased significantly from 73 ± 12.4 preoperatively to 124 ± 8.4 degrees, P < 0.001. The mean graft union time was 4.9 ± 1 months. No significant complications were reported. CONCLUSIONS: Autogenous bone graft reconstruction is a safe and effective method of addressing uncontained medial proximal tibial defects in primary TKA. LEVEL OF EVIDENCE: Level IV.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".