Long-Term Followup of Fresh Femoral Osteochondral Allografts for Posttraumatic Knee Defects
Bibliographic record
Abstract
Fresh osteochondral allografts were used to repair articular defects in the distal femur in 72 patients. Sixty patients were available for long-term followup (mean, 10 years) to determine graft survivorship and patient outcomes using a modified Hospital for Special Surgery score. Twelve of 60 grafts have failed with three having graft removal alone and nine being converted to total knee replacement. Kaplan-Meier survivorship analysis showed 85% graft survival at 10 years and 74% survival at 15 years. Patients with surviving grafts had good function, with a mean Hospital for Special Surgery score of 83 points at 10 years followup. Ten patients (17%) required meniscal transplantation whereas 41 (68%) required realignment osteotomy done simultaneously with the osteochondral allograft. Patients requiring meniscal transplantation, limb realignment, or both, had equally good outcomes at 10 years as those who underwent osteochondral transplantation alone. Likewise, transplantation to the medial or the lateral condyle had no bearing on long-term outcomes. Radiographs were available for 38 patients. These radiographs showed that 18 (48%) patients had no or mild arthritis, 10 (26%) had moderate, and 10 (26%) had severe arthritis. Late osteoarthritic degeneration as seen on radiographs was associated with outcomes, with patients with more severe arthritis having lower Hospital for Special Surgery scores. The authors think that osteochondral allograft transplantation is a valuable treatment option in patients with large osteochondral defects in the distal femoral articular surface.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".