Kopf, Sebastian

Abstract Acute, traumatic meniscus tears are common and should be repaired whenever possible. However, the biological age of the patient, along with the specific tear morphology, may significantly influence the selection of the repair technique, expected clinical outcomes and the postoperative rehabilitation protocol. Risk factors for failure of meniscus repair, such as ligament laxity, need to be addressed to improve the healing potential of the meniscus. Biologic augmentation strategies, including bone marrow stimulation, platelet‐rich plasma, fibrin clot, mesenchymal stem cells or meniscal wrapping, can be used to enhance healing and reduce failure of meniscus repair rates. However, the clinical evidence remains limited and of low quality. As such, the routine use of biologic augmentation in meniscus repair warrants critical evaluation. In patients with post‐meniscectomy syndrome, meniscus allograft transplantation (MAT) may be a viable option to reduce pain and joint degeneration and improve knee function. Meniscus implants provide alternatives to traditional MAT; however, their long‐term efficacy, chondroprotective effects and integration within the knee joint require further research. Meniscus reconstruction using tendon autografts has been described as an alternative treatment option in patients with meniscus loss. Emerging methods, such as gene therapy and repurposed drugs, demonstrate in vitro potential for advancing meniscal repair, although their clinical applications are still in their infancy. Future research is necessary to optimize meniscal repair techniques, enhance clinical efficacy, and ultimately, improve outcomes in patients. Level of Evidence Level V.

Purpose To develop an expert consensus statement on the diagnosis, management, and rehabilitation of medial meniscal root tears (MMRTs) using a modified Delphi technique. Methods A working group developed statements on MMRT diagnosis, nonoperative management, surgical indications, surgical management, alignment, and rehabilitation using modified Delphi techniques. Fifty‐six experts were surveyed over 3 rounds to reach consensus, with agreement measured on a 5‐point Likert scale. Statements were included, revised, or excluded on the basis of predefined thresholds (≥75% agreement, <20% disagreement). Experts suggested revisions or new statements in the first 2 rounds, and final consensus statements were included. Results All 56 experts completed 3 survey rounds. Experts agreed that root tears may occur with no known history of trauma, typically in older patients, and that it should be diagnosed with an magnetic resonance imaging. In symptomatic patients with MMRTs without advanced osteoarthritis should be repaired using an anatomic transtibial pull‐out technique (performing a pie‐crusting technique can be helpful for visualization). Nonoperative management is advised for patients with advanced osteoarthritis. High tibial osteotomy may be considered for significant varus malalignment during MMRT repair. The only statement without consensus was the management of asymptomatic MMRTs with mild medial compartment cartilage wear, indicating ongoing debate. Conclusions Overall, 98% of statements reached consensus. There is agreement that magnetic resonance imaging is the gold standard for diagnosis. Symptomatic MMRTs without advanced osteoarthritis should be repaired early using an anatomic transtibial pull‐out technique. End‐stage knee osteoarthritis warrants nonoperative management of MMRTs, and a structured postoperative protocol with limited weightbearing and range of motion is essential after repair. No agreement was reached on managing asymptomatic MMRTs in patients without significant medial compartment degeneration. Meniscal centralization sutures may help in cases of substantial extrusion, but their routine use is debated. Level of Evidence Level V, consensus of expert opinion.

AbstractArtificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high‐stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real‐world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.Level of EvidenceLevel V.