Long-Term Followup of the Use of Fresh Osteochondral Allografts for Posttraumatic Knee Defects
Bibliographic record
Abstract
UNLABELLED: Posttraumatic osteochondral defects of the distal femur or proximal tibia pose a reconstructive challenge for the young active patient. Fresh osteochondral allografts have been used to reconstruct these defects and this report deals with the long-term clinical and radiographic follow-up in this patient population. This is a prospective nonrandomized study. Sixty patients with an average followup of 10 years received femoral condylar grafts. Twelve grafts failed, requiring removing of the graft in three patients and conversion to total knee replacement in nine patients. Kaplan-Meier survivorship showed 95% graft survival at 5 years and 85% at 10 years. Sixty-five patients received fresh osteochondral allografts to reconstruct the tibial plateau with an average followup of 11.8 years. In this group of patients, conversion to total knee arthroplasty was done in 21 patients at a mean interval of 9.7 years. Survival analysis revealed 95% survival at 5 years, 80% at 10 years, and 65% at 15 years. Through our long-term prospective study, we confirm the value of fresh osteochondral allografts to reconstruct articular defects of the knee in the young active patient. LEVEL OF EVIDENCE: Therapeutic study, Level II-1 (prospective cohort study). See the Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".