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Purpose: The demographic and radiological risk factors of subchondral insufficiency fractures of the knee (SIFK) continue to be a subject of debate. The purpose of this study was to associate patient‐specific factors with SIFK in a large cohort of patients. Methods: Inclusion criteria consisted of patients with SIFK as verified on magnetic resonance imaging (MRI). All radiographs and MRIs were reviewed to assess characteristics such as meniscus tear presence and type, subchondral oedema presence and location, location of SIFK, mechanical limb alignment, osteoarthritis as assessed by Kellgren–Lawrence grade and ligamentous injury. A total of 253 patients (253 knees) were included, with 171 being female. The average body mass index (BMI) was 32.1 ± 7.0 kg/m2. Results: SIFK was more common in patients with medial meniscus tears (77.1%, 195/253) rather than tears of the lateral meniscus (14.6%, 37/253) (p < 0.001). Medial meniscus root and radial tears of the posterior horn were present in 71.1% (180/253) of patients. Ninety‐one percent (164/180) of medial meniscus posterior root and radial tears had an extrusion ≥3.0 mm. Eighty‐one percent (119/147) of patients with SIFK on the medial femoral condyle and 86.8% (105/121) of patients with SIFK on the medial tibial plateau had a medial meniscus tear. Varus knees had a significantly increased rate of SIFK on the medial femoral condyle in comparison to valgus knees (p = 0.016). Conclusion: In this large cohort of patients with SIFK, there was a high association with medial meniscus root and radial tears of the posterior horn, meniscus extrusion ≥3.0 mm as well as higher age, female gender and higher BMI. Additionally, there was a particularly strong association of medial compartment SIFK with medial meniscus tears. As SIFK is frequently undiagnosed, identifying patient‐specific demographic and radiological risk factors will help achieve a prompt diagnosis. Level of Evidence: Level IV. [ABSTRACT FROM AUTHOR]

Background: Based in part on the results of randomized controlled trials (RCTs) that suggest a beneficial effect over alternative treatment options, the use of platelet-rich plasma (PRP) for the management of knee osteoarthritis (OA) is widespread and increasing. However, the extent to which these studies are vulnerable to slight variations in the outcomes of patients remains unknown. Purpose: To evaluate the statistical fragility of conclusions from RCTs that reported outcomes of patients with knee OA who were treated with PRP versus alternative nonoperative management strategies. Study Design: Systematic review and meta-analysis; Level of evidence, 2. Methods: All RCTs comparing PRP with alternative nonoperative treatment options for knee OA were identified. The fragility index (FI) and reverse FI were applied to assess the robustness of conclusions regarding the efficacy of PRP for knee OA. Meta-analyses were performed to determine the minimum number of patients from ≥1 trials included in the meta-analysis for which a modification on the event status would change the statistical significance of the pooled treatment effect. Results: In total, this analysis included outcomes from 1993 patients with a mean ± SD age of 58.0 ± 3.8 years. The mean number of events required to reverse significance of individual RCTs (FI) was 4.57 ± 5.85. Based on random-effects meta-analyses, PRP demonstrated a significantly higher rate of successful outcomes when compared with hyaluronic acid (P =.002; odds ratio [OR], 2.19; 95% CI, 1.33-3.62), as well as higher rates of patient-reported symptom relief (P =.019; OR, 1.55; 95% CI, 1.07-2.24), not requiring a reintervention after the initial injection treatment (P =.002; OR, 2.17; 95% CI, 1.33-3.53), and achieving the minimal clinically important difference (MCID) for pain improvement (P =.007; OR, 6.19; 95% CI, 1.63-23.42) when compared with all alternative nonoperative treatments. Overall, the mean number of events per meta-analysis required to change the statistical significance of the pooled treatment effect was 8.67 ± 4.50. Conclusion: Conclusions drawn from individual RCTs evaluating PRP for knee OA demonstrated slight robustness. On meta-analysis, PRP demonstrated a significant advantage over hyaluronic acid as well as improved symptom relief, lower rates of reintervention, and more frequent achievement of the MCID for pain improvement when compared with alternative nonoperative treatment options. Statistically significant pooled treatment effects evaluating PRP for knee OA are more robust than approximately half of all comparable meta-analyses in medicine and health care. Future RCTs and meta-analyses should consider reporting FIs and fragility quotients to facilitate interpretation of results in their proper context. [ABSTRACT FROM AUTHOR]

Artificial 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 Evidence: Level V. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI‐based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self‐supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI‐driven orthopaedic research. Level of Evidence: Level IV. [ABSTRACT FROM AUTHOR]