Midterm Results of the Treatment of Cartilage Defects in the Knee Using Alginate Beads Containing Human Mature Allogenic Chondrocytes
Bibliographic record
Abstract
BACKGROUND: The treatment of chondral lesions is still an important challenge for the orthopaedic surgeon. Attempts have been made to restore cartilage lesions by filling the defects with a temporary biocompatible matrix. PURPOSE: The authors present their midterm experience with the implantation of alginate beads containing human mature allogenic chondrocytes for the treatment of cartilage lesions in the knee. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: A biodegradable, alginate-based biocompatible scaffold containing human mature allogenic chondrocytes was used for the treatment of cartilage lesions in the knee. Twenty-one patients were clinically prospectively evaluated with use of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and a visual analog scale (VAS). The mean follow-up time was 6.3 years (range, 5-8 years). Magnetic resonance imaging (MRI) data were analyzed based on the MOCART (Magnetic Resonance Observation of Cartilage Repair Tissue) system, allowing morphologic assessment of the repair tissue. Magnetic resonance images were taken at 1 year of follow-up and at a mean follow-up of 6.1 years (range, 5-7 years). RESULTS: During the follow-up period, the WOMAC and VAS scores improved significantly. No signs of clinical deterioration or adverse reactions to the alginate beads/allogenic chondrocyte implantation were observed. Four failures occurred during the follow-up period in this study (19.05%). The MOCART scores were moderate and remained stable in time. CONCLUSION: This investigation provided useful information on the efficacy of the implantation of alginate beads containing human mature allogenic chondrocytes for the treatment of cartilage lesions in the knee. The midterm clinical outcome of the presented technique was satisfactory. However, these results were not confirmed by the MRI findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".