Introduction: Artificial intelligence (AI) programs have the ability to answer complex queries including medical profession examination questions. The purpose of this study was to compare the performance of orthopaedic residents (ortho residents) against Chat Generative Pretrained Transformer (ChatGPT)-3.5 and GPT-4 on orthopaedic assessment examinations. A secondary objective was to perform a subgroup analysis comparing the performance of each group on questions that included image interpretation versus text-only questions. Methods: The ResStudy orthopaedic examination question bank was used as the primary source of questions. One hundred eighty questions and answer choices from nine different orthopaedic subspecialties were directly input into ChatGPT-3.5 and then GPT-4. ChatGPT did not have consistently available image interpretation, so no images were directly provided to either AI format. Answers were recorded as correct versus incorrect by the chatbot, and resident performance was recorded based on user data provided by ResStudy. Results: Overall, ChatGPT-3.5, GPT-4, and ortho residents scored 29.4%, 47.2%, and 74.2%, respectively. There was a difference among the three groups in testing success, with ortho residents scoring higher than ChatGPT-3.5 and GPT-4 (P < 0.001 and P < 0.001). GPT-4 scored higher than ChatGPT-3.5 (P = 0.002). A subgroup analysis was performed by dividing questions into question stems without images and question stems with images. ChatGPT-3.5 was more correct (37.8% vs. 22.4%, respectively, OR = 2.1, P = 0.033) and ChatGPT-4 was also more correct (61.0% vs. 35.7%, OR = 2.8, P < 0.001), when comparing text-only questions versus questions with images. Residents were 72.6% versus 75.5% correct with text-only questions versus questions with images, with no significant difference (P = 0.302). Conclusion: Orthopaedic residents were able to answer more questions accurately than ChatGPT-3.5 and GPT-4 on orthopaedic assessment examinations. GPT-4 is superior to ChatGPT-3.5 for answering orthopaedic resident assessment examination questions. Both ChatGPT-3.5 and GPT-4 performed better on text-only questions than questions with images. It is unlikely that GPT-4 or ChatGPT-3.5 would pass the American Board of Orthopaedic Surgery written examination.

(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon’s NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566–0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS.

Antibiotic bone cement (ABC) is an effective tool for the prophylaxis and treatment of osteomyelitis due to the controlled, sustained release of local antibiotics. ABC has been proven to be effective in the orthopedic fields of arthroplasty and extremity trauma, but the adoption of ABC in spine surgery is limited. The characteristics of ABC make it an optimal solution for treating vertebral osteomyelitis (VO), a serious complication following spine surgery, typically caused by bacterial and sometimes fungal and parasitic pathogens. VO can be devastating, as infection can result in pathogenic biofilms on instrumentation that is dangerous to remove. New techniques, such as kyphoplasty and novel vertebroplasty methods, could amplify the potential of ABC in spine surgery. However, caution should be exercised when using ABC as there is some evidence of toxicity to patients and surgeons, antibiotic allergies, bone cement structural impairment, and possible development of antibiotic resistance. The purpose of this article is to describe the basic science of antibiotic cement utilization and review its usage in spine surgery.

» Coccydynia is a painful condition affecting many patients at the terminal caudal end of the spine. » An understanding of coccyx anatomy and variations of morphology is necessary for proper diagnosis. A multifactorial etiology for pain may be responsible for this clinical entity. » Several treatment options exist. Successful outcomes for patients depend on individual patient characteristics and the etiology of pain.