ArticleOrthopedic reviews2026
Algorithm-Based Surgical Decision-Making for Focal Knee Cartilage Defects: A Pragmatic Clinical Framework with Illustrative Cases.
Article in Orthopedic reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Purpose: To evaluate the early clinical and radiological outcomes of an algorithm-based surgical decision-making framework for focal knee cartilage defects using reproducible, cost-effective techniques in routine clinical practice. Materials and Methods: This retrospective case series included five consecutive patients with ICRS Grade 3-4 chondral or osteochondral lesions. Treatment followed a structured decision-making algorithm based on fragment viability, lesion size, and patient-specific factors. Surgical procedures included bone marrow stimulation (BMS, n=2), osteochondral autograft transplantation (OAT, n=1), and osteochondral fragment refixation for osteochondritis dissecans (OCD, n=2). Clinical outcomes were assessed using the Visual Analogue Scale (VAS) and the Knee Injury and Osteoarthritis Outcome Score (KOOS) at 1, 3, and 6 months. Structural repair was evaluated at 6 months using MRI-based MOCART 2.0 scoring. Results: All patients demonstrated progressive clinical improvement. Mean VAS scores decreased from 6.6 preoperatively to 1.2 at 6 months. KOOS scores improved across all subscales, with the greatest gains observed in activities of daily living. MRI evaluation demonstrated variable structural maturation (mean MOCART 2.0 score: 60, range 45-75), with superior outcomes observed in patients undergoing fragment refixation. Even in an older patient with extensive pathology, bone marrow stimulation resulted in satisfactory short-term outcomes. Conclusion: The proposed algorithm-based approach provides a practical and reproducible framework for selecting surgical treatment in focal knee cartilage defects. Despite the limited sample size, the consistent clinical improvement supports the feasibility and applicability of this decision-making model, particularly in real-world and resource-constrained settings.
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