The latest medical research on Podiatry

The Bartoníček/Rammelt classification is established for posterior malleolar fractures. It subdivides the fractures into 5 types and outlines treatment recommendations. This study aims to determine the intraobserver and interobserver reliability of the Bartoníček/Rammelt classification and investigates its applicability regarding treatment recommendations.

Computed tomography (CT) scans of 80 ankle fractures with a posterior malleolar fracture were analyzed by four observers at two different time points 30 days apart (d1 and d2). Intrarater and interrater reliability was measured using kappa values. The corresponding surgery rates of the fracture subtypes were analyzed, and the surgery rates were correlated with fragment sizes and displacements.

A moderate interobserver reliability for d1 0.41 (CI 0.35-0.47) and d2 0.42 (CI 0.36-0.48) was detected. Intraobserver reliability was documented as perfect, with a mean kappa of 0.83. Type II fractures were operated on in 50% of cases. In 50% of type II cases, a nonoperative treatment was chosen. Fragment size correlated strongly with the chosen therapy, and osteosynthesis was performed significantly more often when the fragment size exceeded 3 cm3 (P < .01).

The Bartoníček/Rammelt classification system showed moderate interobserver reliability and perfect to substantial intraobserver reliability. In clinical practice of this study cohort, the size of the posterior malleolar fragment rather than the dislocation and joint impaction seemed to have the decision to operate on type II or III fractures. Existing treatment recommendations based on the Bartoníček/Rammelt classification correspond to the therapy algorithm carried out in this cohort of patients.Levels of Evidence: Level III: Retrospective study.

Artificial intelligence (AI) large language models (LLMs), such as Chat Generative Pre-trained Transformer (ChatGPT), have gained traction as both augmentative tools in patient care but also as powerful synthesizing machines. The use of ChatGPT in orthopaedic foot and ankle surgery, particularly as an informative resource for patients, has not been described to date. The purpose of this study was to assess the quality of information provided by ChatGPT in response to commonly asked questions about total ankle replacement (TAR).

ChatGPT was asked 10 frequently asked questions about TAR in a conversational thread. Responses were recorded without follow-up, and subsequently graded A, B, C, or F, corresponding with "excellent response," "adequate response needing mild clarification," "inadequate response needing moderate clarification," and "poor response needing severe clarification."

Of the 10 responses, 2 were grade "A," 6 were grade "B," 2 were grade "C," and none were grade "F." Overall, the LLM provided good-quality responses to the posed prompts. Conclusion. Overall, the provided responses were understandable and representative of the current literature surrounding TAR. This study highlights the potential role LLMs in augmenting patient understanding of foot and ankle operative procedures.

IV.

Lisfranc injuries refer to a disruption or displacement of the tarsometatarsal joint of the foot. Subtle Lisfranc injuries can go undiagnosed on conventional imaging leading to devastating consequences and poor functional outcomes for elite athletes. Objective. The objective of this case study is to present a novel imaging technique using weight-bearing computed tomography (CT) with enhanced stress to identify subtle, dynamically unstable Lisfranc injuries. We illustrate this with a case presentation of an elite athlete who ultimately required surgical fixation for a subtle Lisfranc injury.

To perform an augmented stress weight-bearing CT, the patient was positioned standing, with their feet facing forward, and weight equally distributed. The patient was then coached to symmetrically raise both heels from the scanner platform. This plantarflexion provided augmented stress on the midfoot, allowing for more sensitive imaging of the Lisfranc injury. The weight-bearing CT and augmented stress images undergo 3D reconstruction and postprocessing to render coronal and sagittal images, allowing for comparison of the standard weight-bearing and augmented stress images.

We present the case of a 22-year-old collegiate football lineman sustaining a Lisfranc injury. The injury diagnosis was made by magnetic resonance imaging (MRI) and clinical examination, without evidence of injury on weight-bearing XR or standard weight-bearing CT. With augmented stress CT imaging, the Lisfranc instability was noted, leading to surgical fixation, and return to sport the next season.

We propose this technique for diagnosing subtle, unstable Lisfranc injuries where clinical suspicion persists despite inconclusive imaging, particularly in elite athletes. Further research is needed with larger sample sizes to investigate the sensitivity of this novel imaging technique for the detection of Lisfranc injury.

Level 4: Case Report.

The purpose of this study was to perform a comparative analysis of foot and ankle patient education material generated by the AI chatbots, as they compare to the American Orthopaedic Foot and Ankle Society (AOFAS)-recommended patient education website, FootCareMD.org.

ChatGPT, Google Bard, and Bing AI were used to generate patient educational materials on 10 of the most common foot and ankle conditions. The content from these AI language model platforms was analyzed and compared with that in FootCareMD.org for accuracy of included information. Accuracy was determined for each of the 10 conditions on a basis of included information regarding background, symptoms, causes, diagnosis, treatments, surgical options, recovery procedures, and risks or preventions.

When compared to the reference standard of the AOFAS website FootCareMD.org, the AI language model platforms consistently scored below 60% in accuracy rates in all categories of the articles analyzed. ChatGPT was found to contain an average of 46.2% of key content across all included conditions when compared to FootCareMD.org. Comparatively, Google Bard and Bing AI contained 36.5% and 28.0% of information included on FootCareMD.org, respectively (P < .005).

Patient education regarding common foot and ankle conditions generated by AI language models provides limited content accuracy across all 3 AI chatbot platforms.

Level IV.