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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]

Background and Purpose: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models. Methods: Consecutive adult patients with anterior circulation LVOs who were scanned with CT angiography (CTA) and CT perfusion were queried in this single‐center, retrospective study. The favorable outcome was defined as a modified Rankin score (mRS) of 0‐2 at 90 days. Predictor variables included only imaging parameters. CatBoost, XGBoost, and Random Forest were employed. Algorithms were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision‐recall curve (AUPRC), accuracy, Brier score, recall, and precision. SHapley Additive exPlanations were implemented. Results: A total of 180 patients (102 female) were included, with a median age of 69.5. Ninety‐two patients had an mRS between 0 and 2. The best algorithm in terms of AUROC was XGBoost (0.91). Furthermore, the XGBoost model exhibited a precision of 0.72, a recall of 0.81, an AUPRC of 0.83, an accuracy of 0.78, and a Brier score of 0.17. Multiphase CTA collateral score was the most significant feature in predicting the outcome. Conclusions: Using only imaging parameters, our model had an AUROC of 0.91 which was superior to most previous studies, indicating that imaging parameters may be as accurate as conventional predictors. The multiphase CTA collateral score was the most predictive variable, highlighting the importance of collaterals. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti‐epileptic drugs (AEDs), 30%–40% develop drug‐resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More‐efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)–based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields. [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]

While multiple studies exist comparing cervical laminoplasty (CLP) and posterior cervical laminectomy with fusion (PCF), no clear consensus exists on which intervention is better. An umbrella review helps provide an overall assessment by analyzing a given condition's multiple interventions and outcomes. It integrates all available information on a topic and allows a consensus to be reached on the intervention of choice. A literature search was conducted using specific search criteria in PubMed, Scopus, and Web of Science databases. Titles and abstracts were screened based on inclusion criteria. A full-text review of articles that passed the initial inclusion criteria was performed. Nine meta-analyses were deemed eligible for the umbrella review. Data was extracted on reported variables from these meta-analyses. Subsequent quality assessment using AMSTAR2 and data analysis using the R package metaumbrella were used to determine the significance of postoperative outcomes. When the meta-analyses were pooled, statistically significant differences between CLP and PCF were found for postoperative overall complications rate and postoperative JOA score. PCF was associated with a lower overall complication rate and a higher postoperative JOA score, both supported by a weak level of evidence (class IV). Data regarding all other outcomes were non-significant. Our umbrella review investigates CLP and PCF by providing a comprehensive overview of existing evidence and evaluating inconsistencies within the literature. This umbrella review revealed that PCF had better outcomes for overall complications rate and postoperative JOA than CLP, but they were classified as being of weak significance. [ABSTRACT FROM AUTHOR]

The number of scholarly articles continues to rise. The continuous increase in scientific output poses a challenge for researchers, who must devote considerable time to collecting and analyzing these results. The topic modeling approach emerges as a novel response to this need. Considering the swift advancements in computed tomography perfusion (CTP), we deem it essential to launch an initiative focused on topic modeling. We conducted a comprehensive search of the Scopus database from 1 January 2000 to 16 August 2023, to identify relevant articles about CTP. Using the BERTopic model, we derived a group of topics along with their respective representative articles. For the 2020s, linear regression models were used to identify and interpret trending topics. From the most to the least prevalent, the topics that were identified include "Tumor Vascularity", "Stroke Assessment", "Myocardial Perfusion", "Intracerebral Hemorrhage", "Imaging Optimization", "Reperfusion Therapy", "Postprocessing", "Carotid Artery Disease", "Seizures", "Hemorrhagic Transformation", "Artificial Intelligence", and "Moyamoya Disease". The model provided insights into the trends of the current decade, highlighting "Postprocessing" and "Artificial Intelligence" as the most trending topics. [ABSTRACT FROM AUTHOR]

By predicting short-term postoperative outcomes before surgery, patients who undergo posterior cervical fusion (PCF) surgery may benefit from more precise patient care plans that reduce the likelihood of unfavorable outcomes. We developed machine learning models for predicting short-term postoperative outcomes and incorporate these models into an open-source web application in this study. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify patients who underwent PCF surgery. Prolonged length of stay, non-home discharges, and readmissions were the three outcomes that were investigated. To predict these three outcomes, machine learning models were developed and incorporated into an open access web application. A total of 6277 patients that underwent PCF surgery were included in the analysis. The most accurately predicted outcome in terms of the area under the receiver operating characteristic curve (AUROC) was the non-home discharges with a mean AUROC of 0.812, and the most accurately predicting algorithm in terms of AUROC was the LightGBM algorithm with a mean AUROC of 0.766. The following URL will take users to the open access web application created to provide predictions for individual patients based on their characteristics: https://huggingface.co/spaces/MSHS-Neurosurgery-Research/NSQIP-PCF. Machine learning techniques have a significant potential for predicting postoperative outcomes following PCF surgery. The development of predictive models as clinically useful decision-making tools may significantly improve risk assessment and prognosis as the amount of data in spinal surgery keeps growing. Here, we present predictive models for PCF surgery that are meant to accomplish the aforementioned goals and make them publicly available. [ABSTRACT FROM AUTHOR]

This article discusses a case of Twiddler's syndrome, a rare complication of intrathecal baclofen (ITB) therapy, which is used to manage spasticity in patients with conditions such as stroke, cerebral palsy, and traumatic brain injury. Twiddler's syndrome occurs when the catheter connected to the ITB pump becomes twisted or disconnected, leading to baclofen withdrawal and symptom progression. The article describes the case of a 55-year-old woman who experienced progressive spasticity despite uptitration of baclofen over several years. The patient underwent multiple pump revisions and ultimately had a successful surgical revision with a sturdier attachment point for the pump. The article emphasizes the importance of early recognition and management of pump malfunction, as well as the need for multidisciplinary care and communication between physiatrists and neurosurgeons. [Extracted from the article]

Background: Comorbidity scales for outcome prediction in traumatic brain injury (TBI) include the 5-component modified Frailty Index (mFI-5), the 11-component modified Frailty Index (mFI-11), and the Charlson Comorbidity Index (CCI). Objective: To compare the accuracy in predicting clinical outcomes in TBI of mFI-5, mFI-11, and CCI. Methods: The National Trauma Data Bank (NTDB) of the American College of Surgeons (ACS) was utilized to study patients with isolated TBI for the years of 2017 and 2018. After controlling for age and injury severity, individual multivariable logistic regressions were conducted with each of the 3 scales (mFI-5, mFI-11, and CCI) against predefined outcomes, including any complication, home discharge, facility discharge, and mortality. Results: All 3 scales demonstrated adequate internal consistency throughout their individual components (0.63 for mFI-5, 0.60 for CCI, and 0.56 for mFI-11). Almost all studied complications were significantly more likely in frail patients. mFI-5 and mFI-11 had similar areas under the curve (AUC) for all outcomes, while CCI had lower AUCs (0.62-0.61-0.53 for any complication, 0.72-0.72-0.52 for home discharge, 0.78-0.78-0.53 for facility discharge, and 0.71-0.70-0.52 for mortality, respectively). Conclusion: mFI-5 and mFI-11 demonstrated similar accuracy in predicting any complication, home discharge, facility discharge, and mortality in TBI patients across the NTDB. In addition, CCI's performance was poor for the aforementioned metrics. Since mFI-5 is simpler, yet as accurate as the 2 other scales, it may be the most practical both for clinical practice and for future studies with the NTDB. [ABSTRACT FROM AUTHOR]

Background: Degenerative cervical myelopathy (DCM) is a common and disabling condition. Early effective treatment is limited by late diagnosis. Conventional descriptions of DCM focus on motor and sensory limb disability, however, recent work suggests the true impact is much broader. This study aimed to characterise the symptomatic presentation of DCM from the perspective of people with DCM and determine whether any of the reported symptoms, or groups of symptoms, were associated with early diagnosis. Methods: An internet survey was developed, using an established list of patient-reported effects. Participants (N = 171) were recruited from an online community of people with DCM. Respondents selected their current symptoms and primary presenting symptom. The relationship of symptoms and their relationship to time to diagnosis were explored. This included symptoms not commonly measured today, termed 'non-conventional' symptoms. Results: All listed symptoms were experienced by >10% of respondents, with poor balance being the most commonly reported (84.2%). Non-conventional symptoms accounted for 39.7% of symptomatic burden. 55.4% of the symptoms were reported as an initial symptom, with neck pain the most common (13.5%). Non-conventional symptoms accounted for 11.1% of initial symptoms. 79.5% of the respondents were diagnosed late (>6 months). Heavy legs was the only initial symptom associated with early diagnosis. Conclusions: A comprehensive description of the self-reported effects of DCM has been established, including the prevalence of symptoms at disease presentation. The experience of DCM is broader than suggested by conventional descriptions and further exploration of non-conventional symptoms may support earlier diagnosis. [ABSTRACT FROM AUTHOR]