The Effectiveness of Various Surgical Techniques in the Treatment of Local Knee Cartilage Lesions (Review)
Bibliographic record
Abstract
Introduction. To restore the knee local cartilage lesions, a large number of alternative surgical techniques are used in clinical practice: isolated debridement of the lesion area, chondrogenesis stimulation, mosaic osteochondral grafting, cell technologies, collagen membranes (matrices), and a combination of the above methods. The purpose of this article was to compare the effectiveness of various surgical methods of treating patients with local cartilage lesions of the femur based on analysis of relevant publications. Materials and Methods. The review included 85 publications of domestic and foreign authors within 2005 to 2020. The search was carried out in electronic scientific databases PubMed and eLIBRARy. Results. The medium and long term outcomes of debridement and/or various options of chondrogenesis stimulating, despite their wide popularity, in terms of clinical, radiological, and histological indicators, are inferior to all other surgical techniques. Mosaic osteochondral auto- and/or allografting, as well as transplantation of autologous chondrocytes culture with a collagen membrane, are characterized by the best 15 to 20-year outcomes, allowing most patients to maintain the same level of activity as before the lesion occurred. The combination of matrices with other cellular products or microfracturing shows similar medium-term results, but it long-term efficacy remains unknown. Conclusion. The use of debridement and/or chondrogenesis stimulation should be limited to minimal defects. From both a clinical and an economic point of view, mosaic osteochondral grafting is the optimal method for the treatment of knee local cartilage lesions with an area up to 4 to 6 cm2 . The combination of membranes with various cellular products or microfracturing is indicated in case of extensive local cartilage lesions or if mosaic osteochondral grafting is not appropriate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".