The Evaluation of Cartilage Regeneration Efficacy of Three-Dimensionally Biofabricated Human-Derived Biomaterials on Knee Osteoarthritis: A Single-Arm, Open Label Study in Egypt
Bibliographic record
Abstract
Full thickness cartilage defects in cases of knee osteoarthritis are challenging in nature and are difficult to treat. The implantation of three-dimensional (3D) biofabricated grafts into the defect site can be a promising biological one-stage solution for such lesions that can avoid different disadvantages of the alternative surgical treatment options. In this study, the short-term clinical outcome of a novel surgical technique that uses a 3D bioprinted micronized adipose tissue (MAT) graft for knee cartilage defects is assessed and the degree of incorporation of such graft types is evaluated via arthroscopic and radiological analyses. Ten patients received 3D bioprinted grafts consisting of MAT with an allogenic hyaline cartilage matrix on a mold of polycaprolactone, with or without adjunct high tibial osteotomy, and they were monitored until 12 months postoperatively. Clinical outcomes were examined with patient-reported scoring instruments that consisted of the Western Ontario and McMaster Universities Arthritis Index (WOMAC) score and the Knee Injury and Osteoarthritis Outcome Score (KOOS). The graft incorporation was assessed using the Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score. At 12 months follow-up, cartilage tissue biopsy samples were taken from patients and underwent histopathological examination. In the results, at final follow-up, the WOMAC and KOOS scores were 22.39 ± 7.7 and 79.16 ± 5.49, respectively. All scores were significantly increased at final follow-up (p < 0.0001). MOCART scores were also improved to a mean of 82.85 ± 11.49, 12 months after operation, and we observed a complete incorporation of the grafts with the surrounding cartilage. Together, this study suggests a novel regeneration technique for the treatment of knee osteoarthritis patients, with less rejection response and better efficacy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".