Mid-term results of impaction bone grafting in tibial bone defects in complex primary knee arthroplasty for severe varus deformity
Bibliographic record
Abstract
INTRODUCTION: Bone defects are a challenging problem encountered occasionally during primary knee arthroplasty. These defects should be meticulously addressed so as to avoid malalignment and premature loosening and failure. Out of the many options available to deal with these defects, impaction bone grafting provides a more biological solution, which is especially important in case of primary knees. MATERIALS AND METHODS: A retrospective analysis was done and patients with severe varus deformity of more than 20 degrees who had undergone primary knee arthroplasty with impaction bone grafting of the tibial condyle defect were followed up. RESULTS: Between 2008 and 2014, out of the 1124 patients who underwent primary total knee arthroplasty, only 26 knees in 23 patients met the inclusion criteria. The amount of varus deformity ranged from 20 to 35 degrees. Follow-up ranged from 3 to 8 years with an average of 6 years. The average pre-operative Knee Society Score (KSS) and Western Ontario McMaster Universities (WOMAC) score were 24.2 and 78, respectively. There were significant improvements in the post-op scores, with the average KSS being 90.2 and the WOMAC being 38. CONCLUSION: Impaction bone grafting provides an invaluable option to the orthopedic surgeon for managing bone defects, especially in case of primary knee arthroplasty as it reconstitutes the bone stock.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".