Use of large osteochondral allografts in reconstruction of traumatic uncontained distal femoral defects
Bibliographic record
Abstract
UNLABELLED: Large osteoarticular injuries with subchondral bone loss involving the knee in young active patients often result in significant morbidity and loss of normal joint function. A review of the current literature reveals that multiple surgical management options are currently employed, however there is no consensus on standard of care. Osteochondral allografting provides an attractive alternative treatment option for the repair of large articular defects of the knee. METHODS: In this article we present the case of a young male who suffered traumatic intraarticular bone loss secondary to a grade IIIA distal femoral fracture and subsequently underwent reconstruction of his medial femoral condyle using a fresh-frozen osteochondral allograft. RESULTS: We present the radiographic and functional outcome of this patient at two years post-operative. The range of motion of the knee was 0-130° and the patient's post-operative functional outcome was evaluated using the Knee injury and Osteoarthritis Outcome Score (KOOS), which was 76%. CONCLUSIONS: While further research is required, the results of our case study concur with the current body of literature supporting the use of fresh-frozen osteochondral allograft as a reconstructive option for treating large traumatic intraarticular lesions involving the distal femur.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".