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STUDY DESIGN: Delayed diagnosis of degenerative cervical myelopathy (DCM) is likely due to a combination of its subtle symptoms, incomplete neurological assessments by clinicians and a lack of public and professional awareness. Diagnostic criteria for DCM will likely facilitate earlier referral for definitive management. OBJECTIVES: This systematic review aims to determine (i) the diagnostic accuracy of various clinical signs and (ii) the association between clinical signs and disease severity in DCM? METHODS: A search was performed to identify studies on adult patients that evaluated the diagnostic accuracy of a clinical sign used for diagnosing DCM. Studies were also included if they assessed the association between the presence of a clinical sign and disease severity. The QUADAS-2 tool was used to evaluate the risk of bias of individual studies. RESULTS: This review identified eleven studies that used a control group to evaluate the diagnostic accuracy of various signs. An additional 61 articles reported on the frequency of clinical signs in a cohort of DCM patients. The most sensitive clinical tests for diagnosing DCM were the Tromner and hyperreflexia, whereas the most specific tests were the Babinski, Tromner, clonus and inverted supinator sign. Five studies evaluated the association between the presence of various clinical signs and disease severity. There was no definite association between Hoffmann sign, Babinski sign or hyperreflexia and disease severity. CONCLUSION: The presence of clinical signs suggesting spinal cord compression should encourage health care professionals to pursue further investigation, such as neuroimaging to either confirm or refute a diagnosis of DCM.

STUDY DESIGN: Delayed diagnosis of degenerative cervical myelopathy (DCM) is associated with reduced quality of life and greater disability. Developing diagnostic criteria for DCM has been identified as a top research priority. OBJECTIVES: This scoping review aims to address the following questions: What is the diagnostic accuracy and frequency of clinical symptoms in patients with DCM? METHODS: A scoping review was conducted using a database of all primary DCM studies published between 2005 and 2020. Studies were included if they (i) assessed the diagnostic accuracy of a symptom using an appropriate control group or (ii) reported the frequency of a symptom in a cohort of DCM patients. RESULTS: This review identified three studies that discussed the diagnostic accuracy of various symptoms and included a control group. An additional 58 reported on the frequency of symptoms in a cohort of patients with DCM. The most frequent and sensitive symptoms in DCM include unspecified paresthesias (86%), hand numbness (82%) and hand paresthesias (79%). Neck and/or shoulder pain was present in 51% of patients with DCM, whereas a minority had back (19%) or lower extremity pain (10%). Bladder dysfunction was uncommon (38%) although more frequent than bowel (23%) and sexual impairment (4%). Gait impairment is also commonly seen in patients with DCM (72%). CONCLUSION: Patients with DCM present with many different symptoms, most commonly sensorimotor impairment of the upper extremities, pain, bladder dysfunction and gait disturbance. If patients present with a combination of these symptoms, further neuroimaging is indicated to confirm the diagnosis of DCM.

Study Design: A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). Purpose: To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. Overview of Literature: Based on our literature review, no study has examined the capability of ML algorithms to predict major short-term ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. Methods: The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. Results: The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. Conclusions: This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes. [ABSTRACT FROM AUTHOR]

Background and Objectives: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. Methods: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. Results: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. Conclusions: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) affects 69 million people worldwide each year, and acute traumatic epidural hematoma (atEDH) is a frequent and severe consequence of TBI. The aim of the study is to use machine learning (ML) algorithms to predict in-hospital death, non-home discharges, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients with atEDH and incorporate the resulting ML models into a user-friendly web application for use in the clinical settings. The American College of Surgeons (ACS) Trauma Quality Program (TQP) database was used to identify patients with atEDH. Four ML algorithms (XGBoost, LightGBM, CatBoost, and Random Forest) were utilized, and the best performing models were incorporated into an open-access web application to predict the outcomes of interest. The study found that the ML algorithms had high area under the receiver operating characteristic curve (AUROC) values in predicting outcomes for patients with atEDH. In particular, the algorithms had an AUROC value range of between 0.874 to 0.956 for in-hospital mortality, 0.776 to 0.798 for non-home discharges, 0.737 to 0.758 for prolonged LOS, 0.712 to 0.774 for prolonged ICU-LOS, and 0.674 to 0.733 for major complications. The following link will take users to the open-access web application designed to generate predictions for individual patients based on their characteristics: huggingface.co/spaces/MSHS-Neurosurgery-Research/TQP-atEDH. This study aimed to improve the prognostication of patients with atEDH using ML algorithms and developed a web application for easy integration in clinical practice. It found that ML algorithms can aid in risk stratification and have significant potential for predicting in-hospital outcomes. Results demonstrated excellent performance for predicting in-hospital death and fair performance for non-home discharges, prolonged LOS and ICU-LOS, and poor performance for major complications. [ABSTRACT FROM AUTHOR]

Background: Degenerative cervical myelopathy (DCM), a progressive spinal cord injury caused by spinal cord compression from degenerative pathology, often presents with neck pain, sensorimotor dysfunction in the upper or lower limbs, gait disturbance, and bladder or bowel dysfunction. Its symptomatology is very heterogeneous, making early detection as well as the measurement or understanding of the underlying factors and their consequences challenging. Increasingly, evidence suggests that DCM may consist of subgroups of the disease, which are yet to be defined., Objective: This study aimed to explore whether machine learning can identify clinically meaningful groups of patients based solely on clinical features., Methods: A survey was conducted wherein participants were asked to specify the clinical features they had experienced, their principal presenting complaint, and time to diagnosis as well as demographic information, including disease severity, age, and sex. K-means clustering was used to divide respondents into clusters according to their clinical features using the Euclidean distance measure and the Hartigan-Wong algorithm. The clinical significance of groups was subsequently explored by comparing their time to presentation, time with disease severity, and other demographics., Results: After a review of both ancillary and cluster data, it was determined by consensus that the optimal number of DCM response groups was 3. In Cluster 1, there were 40 respondents, and the ratio of male to female participants was 13:21. In Cluster 2, there were 92 respondents, with a male to female participant ratio of 27:65. Cluster 3 had 57 respondents, with a male to female participant ratio of 9:48. A total of 6 people did not report biological sex in Cluster 1. The mean age in this Cluster was 56.2 (SD 10.5) years; in Cluster 2, it was 54.7 (SD 9.63) years; and in Cluster 3, it was 51.8 (SD 8.4) years. Patients across clusters significantly differed in the total number of clinical features reported, with more clinical features in Cluster 3 and the least clinical features in Cluster 1 (Kruskal-Wallis rank sum test: χ 2 2 =159.46; P<.001). There was no relationship between the pattern of clinical features and severity. There were also no differences between clusters regarding time since diagnosis and time with DCM., Conclusions: Using machine learning and patient-reported experience, 3 groups of patients with DCM were defined, which were different in the number of clinical features but not in the severity of DCM or time with DCM. Although a clearer biological basis for the clusters may have been missed, the findings are consistent with the emerging observation that DCM is a heterogeneous disease, difficult to diagnose or stratify. There is a place for machine learning methods to efficiently assist with pattern recognition. However, the challenge lies in creating quality data sets necessary to derive benefit from such approaches., (©Alvaro Yanez Touzet, Tanzil Rujeedawa, Colin Munro, Konstantinos Margetis, Benjamin M Davies. Originally published in JMIR Formative Research (https://formative.jmir.org), 25.01.2024.)

Artificial intelligence (AI) and generative language models (GLMs) present significant opportunities for enhancing medical education, including the provision of realistic simulations, digital patients, personalized feedback, evaluation methods, and the elimination of language barriers. These advanced technologies can facilitate immersive learning environments and enhance medical students' educational outcomes. However, ensuring content quality, addressing biases, and managing ethical and legal concerns present obstacles. To mitigate these challenges, it is necessary to evaluate the accuracy and relevance of AI-generated content, address potential biases, and develop guidelines and policies governing the use of AI-generated content in medical education. Collaboration among educators, researchers, and practitioners is essential for developing best practices, guidelines, and transparent AI models that encourage the ethical and responsible use of GLMs and AI in medical education. By sharing information about the data used for training, obstacles encountered, and evaluation methods, developers can increase their credibility and trustworthiness within the medical community. In order to realize the full potential of AI and GLMs in medical education while mitigating potential risks and obstacles, ongoing research and interdisciplinary collaboration are necessary. By collaborating, medical professionals can ensure that these technologies are effectively and responsibly integrated, contributing to enhanced learning experiences and patient care., (©Mert Karabacak, Burak Berksu Ozkara, Konstantinos Margetis, Max Wintermark, Sotirios Bisdas. Originally published in JMIR Medical Education (https://mededu.jmir.org), 06.06.2023.)

Background: Pancreatic cancer is an aggressive disease which metastasizes readily. The presence of brain metastases from pancreatic cancer is rare and it carries a poor prognosis. Our approach to treating these lesions stresses extensive use of stereotactic radiosurgery (SRS), whereas other reports focus on surgical resection., Case Report: Information regarding the patient's clinical history was extracted from a retrospective review of the medical records and imaging studies. The patient survived seven years after his primary diagnosis of pancreatic cancer, and 36 months after diagnosis of metastatic disease to the brain. In addition to surgical resection and the use of multiple chemotherapeutic agents, the patient received six separate radiosurgery treatments., Conclusion: We present a case of brain metastasis from pancreatic cancer that is remarkable for an unusually long survivorship and discuss the utility of SRS along with a multimodality treatment approach for dealing with these cases.