Evolution of a Novel Tissue Preservation Protocol to Optimize Osteochondral Transplantation Outcomes
Bibliographic record
Abstract
OBJECTIVE: Osteochondral allograft transplantation is a procedure to treat focal osteochondral lesions (OCLs), but is limited by tissue availability, the quality of transplanted tissue, and inconsistent storage protocols. The objective of this study was to assess the clinical outcomes of a novel tissue procurement, storage, and quality control protocol in treating OCLs. DESIGN: Prospective case series. Donor cadaveric tissue was processed, stored, and the tissue quality analyzed using the unique tissue preservation protocol developed at our institution. Advanced cross-sectional imaging was used to size match donor tissue with recipient patients. Osteochondral allografts were transplanted using the Arthrex Allograft OATS. Patients were evaluated with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee injury and Osteoarthritis Outcome Score (KOOS), visual analog scale (VAS), and 36-Item Short Form Survey (SF-36) preoperatively and at 1 year and 2 years postoperatively. RESULTS: Twenty patients (17 knees, 3 shoulders) were included in the study. There was a significant improvement in the following scores: overall WOMAC score, WOMAC function and pain subcategories; KOOS pain, knee-related symptoms, activities of daily living, sports and recreation, and quality of life; SF-36 physical functioning, physical role, pain, and social functioning subcategories; and VAS at all time points postoperatively. There was a significant improvement in WOMAC stiffness at 2 years postoperatively. There were 2 failures, defined by graft subsidence and persistent pain requiring reoperation. CONCLUSION: The protocol developed at our institution for OAT resulted in significant clinical improvement in patients with OCLs and is an improvement on existing tissue storage techniques.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".