Arthroscopic Autologous Minced Cartilage Implantation of Cartilage Defects in the Knee: A 2-Year Follow-up of 62 Patients
Bibliographic record
Abstract
Background: Symptomatic cartilage defects of the knee joint are frequently diagnosed and can be treated with different available surgical methods. Nevertheless, there is currently no gold standard treatment for all indications. Minced cartilage implantation is increasingly coming into focus as a refined surgical technique. Purpose: To investigate the 2-year clinical and radiological outcomes of arthroscopic autologous minced cartilage repair with the standardized commercial implantation system AutoCart. Study Design: Case series; Level of evidence, 4. Methods: A total of 62 consecutive patients were included and prospectively evaluated preoperatively and at 3, 6, 12, and 24 months postoperatively. Outcomes were assessed using the Knee injury and Osteoarthritis Outcome Score (KOOS), visual analog scale (VAS) for pain, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Single Assessment Numeric Evaluation (SANE), and Tegner activity scale at all follow-up time points. The examination of preoperative magnetic resonance imaging (MRI) was performed using the Area Measurement and Depth and Underlying Structures (AMADEUS) score, and the examination of MRI at 24 months was performed using the Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) 2.0 score. Results: < .001). The secondary outcome measures of the VAS, WOMAC, and SANE showed a similar pattern, with score improvements in the follow-up period compared to baseline. The mean AMADEUS score was 64.75 ± 13.87, while the mean MOCART 2.0 score was 62.88 ± 9.86, among 20 available patients. The revision surgery rate was 8.1% mainly because of hypertrophy (6.5%). Conclusion: Among this cohort of patients, minced cartilage implantation demonstrated satisfying 2-year outcomes with increased patient-reported outcome measure scores from 3 to 24 months postoperatively. Regenerated tissue quality on MRI was comparable to that using other cartilage repair methods and showed no associations with patient characteristics or patient-reported outcome measures. Larger cohorts, longer postoperative intervals, and comparable trials are needed to further evaluate the role of this technique in treating cartilage defects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".