Functional and Radiological Outcomes of Allograft Application in Distal Femur Fractures: A Retrospective Study
Bibliographic record
Abstract
Background Distal femur fractures are complex injuries requiring surgical intervention, particularly in cases involving bone loss, comminution, or osteoporosis. While autografts remain the preferred option for bone grafting due to their osteogenic, osteoinductive, and osteoconductive properties, their use is often restricted due to donor-site morbidity, limited availability, and increased surgical time. Allografts provide an alternative, offering structural support without additional surgical site morbidity. This study evaluates the radiological and functional outcomes of allograft application in distal femur fractures. Methods A retrospective study was conducted at R. L. Jalappa Hospital, Kolar, analyzing 23 patients who underwent allograft-assisted distal femur fracture fixation between May 2023 and April 2024. The inclusion criteria were distal femur fractures requiring allograft augmentation, while exclusion criteria included pathological fractures, severe infections, and polytrauma patients. Radiological union was assessed using serial X-rays at three and six months, evaluating callus formation and cortical bridging. Pain relief was measured using the visual analog scale (VAS) at each follow-up. Functional recovery was evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC score), assessing pain, stiffness, and physical function. Results Radiological signs of union were achieved in most patients within three to six months. By three months, radiological healing was observed in 11 (47.8%) of cases, and by six months, 17 (73.9%) of patients achieved cortical union. Pain score improved significantly over time, with VAS scores decreasing from 6-8 at one month to 1-3 at six months. Functional recovery followed a similar pattern, with WOMAC scores improving from 65-80 at one month to 85-98 at six months, indicating substantial progress in joint mobility, weight-bearing ability, and pain reduction. Conclusion The findings suggest that allografts provide effective structural support and facilitate bone healing in distal femur fractures, particularly in cases with bone loss. Radiological union is slightly slower than the autografts, and the functional outcomes remain comparable. Allografts serve as a viable option for patients with large bone defects or contraindications for autograft harvesting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".