Osteochondral Repair with Autologous Cartilage Transplantation with or without Bone Grafting: A Short Pilot Study in Mini-Pigs
Bibliographic record
Abstract
ObjectiveTreatment strategies for osteochondral defects, for which particulated autologous cartilage transplantation (PACT) is an emerging treatment strategy, aim to restore the structure and function of the hyaline cartilage. Herein, we compared the efficacy of PACT with control or human transforming growth factor-β (rhTGF-β), and clarified the necessity of bone graft (BG) with PACT to treat shallow osteochondral defects in a porcine model.DesignTwo skeletally mature male micropigs received 4 osteochondral defects in each knee. The 16 defects were randomized to (1) empty control, (2) PACT, (3) PACT with BG, or (4) rhTGF-β. Animals were euthanized after 2 months and histomorphometry, immunofluorescence analysis, semiquantitative evaluation (O’Driscoll score), and magnetic resonance observation of cartilage repair tissue (MOCART) score were performed.ResultsHyaline cartilages, glycosaminoglycan synthesis, and collagen type II staining were more abundant in the PACT than in the control and rhTGF-β groups. The O’Driscoll score was significantly different between groups (P < 0.001), with both PACT groups showing superiority (P = 0.002). PACT had the highest score (P = 0.002), with improved restoration of subchondral bone compared with PACT with BG. The MOCART score showed significant differences between groups (P = 0.021); MOCART and O’Driscoll scores showed high correlation (r = 0.847, P < 0.001).ConclusionTreatment of osteochondral defects with PACT improved tissue quality compared with that with control or rhTGF-β in a porcine model. BG, in addition to PACT, may be unnecessary for shallow osteochondral defects. Clinical Relevance. BG may not be necessary while performing PACT.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".