Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach.

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.

Comprehensive assessment of atypical and anaplastic pediatric meningiomas utilizing national cancer database: a retrospective cohort study.

PURPOSE: To examine demographic and clinical characteristics and their association with survival in grade 2 and 3 pediatric meningiomas in a large cohort using the National Cancer Database (NCDB). METHODS: We conducted a comprehensive analysis using data from NCDB between 2004 to 2018. Tumor-specific data included tumor grade and size. Treatment details, including surgical resection, extent of resection, and radiotherapy, were gathered. Our analytic approach incorporated logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: Among the included 239 patients aged 0-21 years, age category distribution was significantly different between grade 2 and grade 3 tumors (p = 0.018). For grade 2 meningiomas, 51.5% of patients were female, and 76.7% were white. 85.3% of patients with grade 2 meningiomas underwent surgical resection, of which 67% underwent gross total resection. Overall survival (OS) was significantly different between resected and non-resected patients (p = 0.048). Uninsured patients were over seven times as likely to have prolonged length of stay (LOS) versus those with private insurance (OR = 7.663, p = 0.014). For grade 3 meningiomas, 51.4% of patients were male, and 82.9% were white. 91.4% of patients with grade 3 meningiomas underwent surgical resection, of which 53.3% underwent subtotal resection. OS was not significantly different between resected and non-resected patients (p = 0.659). CONCLUSION: In summary, there were significant differences in age, maximum tumor dimension, unplanned readmission, radiotherapy, and treatment combinations between grade 2 and 3 meningiomas. These findings highlight the intricacies of managing pediatric meningiomas and emphasize the necessity for tailored therapeutic approaches to enhance outcomes in the future.

Exploring the differences in radiologic and clinical outcomes of transforaminal lumbar interbody fusion with single- and bi-planar expandable cages: a systematic review and meta-analysis.

Transforaminal lumbar interbody fusion (TLIF) is a universal surgical technique used to achieve lumbar fusion. Traditionally static cages have been used to restore the disc space after discectomy. However, newer technological advancements have brought up uniplanar expandable cages (UECs) and more recently bi-planar expandable cages (BECs), the latter with the hope of reducing the events of intra- or postoperative subsidence compared to UECs. However, since BECs are relatively new, there has been no comparison to UECs. In this PRISMA-compliant systematic review, we sought to identify all Medline and Embase reports that used UECs and/or BECs for TLIF or posterior lumbar interbody fusion. Primary outcomes included subsidence and fusion rates. Secondary outcomes included VAS back pain score, VAS leg pain score, ODI, and other complications. A meta-analysis of proportions was the main method used to evaluate the extracted data. Bias was assessed using the ROBINS-I tool. A total of 15 studies were pooled in the analysis, 3 of which described BECs. There were no studies directly comparing the UECs to BECs. A statistically significant difference in fusion rates was found between UECs and BECs (p = 0.04). Due to lack of direct comparative literature, definitive conclusions cannot be made about differences between UECs and BECs. The analysis showed a statistically higher fusion rate for BECs versus UECs, but this should be interpreted cautiously. No other statistically significant differences were found. As more direct comparative studies emerge, future meta-analyses may clarify potential differences between these cage types.

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

Atypical and anaplastic meningiomas in the later decades of life: A national cancer database analysis.

PURPOSE: We conducted a National Cancer Database (NCDB) study to investigate the epidemiological characteristics and identify predictors of outcomes associated with geriatric meningiomas. METHODS: The NCDB was queried for adults aged 60-89 years diagnosed between 2010 and 2017 with grade 2 and 3 meningiomas. The patients were classified into three age groups based on their age: 60-69 (hexagenarians), 70-79 (septuagenarians), and 80-89 (octogenarians). The log-rank test was utilized to compare the differences in overall survival (OS). Univariate and multivariate Cox proportional hazards regressions were used to evaluate the mortality risk associated with various patient and disease parameters. RESULTS: A total of 6585 patients were identified. Hexagenerians were the most common age group (49.8%), with the majority of meningiomas being classified as grade 2 (89.5%). The incidence of high-grade meningiomas increased in all age groups during the study period. Advanced age, male sex, black race, lower socioeconomic status, Charlson-Deyo score ≥ 2, and higher tumor grade were independent factors of poor survival. Among the modes of treatment, the extent of surgical resection, adjuvant radiotherapy, and treatment at a noncommunity cancer program were linked with better outcomes. CONCLUSION: In geriatric patients with high-grade meningiomas, the greater extent of surgical resection and radiotherapy are associated with improved survival. However, the management and outcome of geriatric patients with higher-grade meningiomas are also associated with several socioeconomic factors.

Trends in stroke-related journals: Examination of publication patterns using topic modeling.

OBJECTIVES: This study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literature review process, reveal hidden themes, and track rising research areas. MATERIALS AND METHODS: Our study involved reviewing and analyzing articles published in five prestigious stroke journals, namely Stroke, International Journal of Stroke, European Stroke Journal, Translational Stroke Research, and Journal of Stroke and Cerebrovascular Diseases. The team extracted document titles, abstracts, publication years, and citation counts from the Scopus database. BERTopic was chosen as the topic modeling technique. Using linear regression models, current stroke research trends were identified. Python 3.1 was used to analyze and visualize data. RESULTS: Out of the 35,779 documents collected, 26,732 were classified into 30 categories and used for analysis. "Animal Models," "Rehabilitation," and "Reperfusion Therapy" were identified as the three most prevalent topics. Linear regression models identified "Emboli," "Medullary and Cerebellar Infarcts," and "Glucose Metabolism" as trending topics, whereas "Cerebral Venous Thrombosis," "Statins," and "Intracerebral Hemorrhage" demonstrated a weaker trend. CONCLUSIONS: The methodology can assist researchers, funders, and publishers by documenting the evolution and specialization of topics. The findings illustrate the significance of animal models, the expansion of rehabilitation research, and the centrality of reperfusion therapy. Limitations include a five-journal cap and a reliance on high-quality metadata.

Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models.

PURPOSE: The primary purpose of this study was to utilize machine learning (ML) models to create a web application that can predict survival outcomes for patients diagnosed with atypical and anaplastic meningiomas. METHODS: In this retrospective cohort study, patients diagnosed with WHO grade II and III meningiomas were selected from the National Cancer Database (NCDB) to analyze survival outcomes at 12, 36, and 60 months. Five machine learning algorithms - TabPFN, TabNet, XGBoost, LightGBM, and Random Forest were employed and optimized using the Optuna library for hyperparameter tuning. The top-performing models were then deployed into our web-based application. RESULTS: From the NCDB, 12,197 adult patients diagnosed with histologically confirmed WHO grade II and III meningiomas were retrieved. The mean age was 61 (± 20), and 6,847 (56.1%) of these were females. Performance evaluation indicated that the top-performing models for each outcome were the models built with the TabPFN algorithm. The TabPFN models yielded area under the receiver operating characteristic (AUROC) values of 0.805, 0.781, and 0.815 in predicting 12-, 36-, and 60-month mortality, respectively. CONCLUSION: With the continuous growth of neuro-oncology data, ML algorithms act as key tools in predicting survival outcomes for WHO grade II and III meningioma patients. By incorporating these interpretable models into a web application, we can practically utilize them to improve risk evaluation and prognosis for meningioma patients.

Radiomics-based machine learning models in STEMI: a promising tool for the prediction of major adverse cardiac events.

OBJECTIVE: To evaluate the potential value of the machine learning (ML) models using radiomic features of late gadolinium enhancement (LGE) and cine images on magnetic resonance imaging (MRI) along with relevant clinical information and conventional MRI parameters for the prediction of major adverse cardiac events (MACE) in ST-segment elevation myocardial infarction (STEMI) patients. METHODS: This retrospective study included 60 patients with the first STEMI. MACE consisted of new-onset congestive heart failure, ventricular arrhythmia, and cardiac death. Radiomic features were extracted from cine and LGE images. Inter-class correlation coefficients (ICCs) were calculated to assess inter-observer reproducibility. LASSO (least absolute shrinkage and selection operator) method was used for radiomic feature selection. Seven separate models using a different combination of the available information were investigated. Classifications with repeat random sampling were done using adaptive boosting, k-nearest neighbor, naive Bayes, neural network, random forest, stochastic gradient descent, and support vector machine algorithms. RESULTS: Of the 1748 extracted radiomic features, 1393 showed good inter-observer agreement. With LASSO, 25 features were selected. Among the ML algorithms, the neural network showed the highest predictive performance on average (area under the curve (AUC) 0.822 ± 0.181). Of the best-calculated model, the one using clinical parameters, CMRI parameters, and selected radiomic features (model 7), the diagnostic performance was as follows: 0.965 AUC, 0.894 classification accuracy, 0.906 sensitivity, 0.883 specificity, 0.875 positive predictive value (PPV), and 0.912 negative predictive value (NPV). CONCLUSION: The radiomics-based ML models incorporating clinical and conventional MRI parameters are promising for predicting MACE occurrence in STEMI patients in the follow-up period. KEY POINTS: • Acute coronary occlusion results in variable changes at the cellular level ranging from myocyte swelling to myonecrosis depending on the duration of the ischemia and the metabolic state of the heart, which causes subtle heterogeneous signal changes that are imperceptible to the human eye with cardiac MRI. • Radiomics-based machine learning analysis of cardiac MR images is promising for risk prediction. • Combining MRI-derived parameters and clinical variables increases the accuracy of predictive models.

Cervical laminoplasty versus laminectomy and fusion: An umbrella review of postoperative outcomes.

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.

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance.

BACKGROUND: Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. PURPOSE: To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. MATERIAL AND METHODS: A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. RESULTS: Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. CONCLUSION: Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.

Development of personalized machine learning-based prediction models for short-term postoperative outcomes in patients undergoing cervical laminoplasty.

PURPOSE: By predicting short-term postoperative outcomes before surgery, patients undergoing cervical laminoplasty (CLP) surgery could benefit from more accurate patient care strategies that could reduce the likelihood of adverse outcomes. With this study, we developed a series of machine learning (ML) models for predicting short-term postoperative outcomes and integrated them into an open-source online application. METHODS: National surgical quality improvement program database was utilized to identify individuals who have undergone CLP surgery. The investigated outcomes were prolonged length of stay (LOS), non-home discharges, 30-day readmissions, unplanned reoperations, and major complications. ML models were developed and implemented on a website to predict these three outcomes. RESULTS: A total of 1740 patients that underwent CLP were included in the analysis. Performance evaluation indicated that the top-performing models for each outcome were the models built with TabPFN and LightGBM algorithms. The TabPFN models yielded AUROCs of 0.830, 0.847, and 0.858 in predicting non-home discharges, unplanned reoperations, and major complications, respectively. The LightGBM models yielded AUROCs of 0.812 and 0.817 in predicting prolonged LOS, and 30-day readmissions, respectively. CONCLUSION: The potential of ML approaches to predict postoperative outcomes following spine surgery is significant. As the volume of data in spine surgery continues to increase, the development of predictive models as clinically relevant decision-making tools could significantly improve risk assessment and prognosis. Here, we present an accessible predictive model for predicting short-term postoperative outcomes following CLP intended to achieve the stated objectives.

Final-year medical student mental wellness during preparation for the examination for specialty in Turkey: a cross-sectional survey study.

BACKGROUND: In Turkey, most final-year medical students prepare for the Examination for Specialty in Medicine in a high-stress environment. To the best of our knowledge, this is the first study on final-year medical student general psychological distress during preparation for the Examination for Specialty in Turkey. We aim to evaluate psychological distress and understand the variables associated with depression, anxiety, and stress levels among final-year medical students preparing for the Examination for Specialty. METHODS: A self-reporting, anonymous, cross-sectional survey with 21 items consisting of demographic variables, custom variables directed for this study, and the DASS-21 was utilized. Survey results were expounded based on univariate analysis and multivariate linear regression analysis. RESULTS: Our study revealed four variables associated with impaired mental wellness among final-year medical students during preparation for the examination for Specialty: attendance to preparatory courses, duration of preparation, consideration of quitting studying, and psychiatric drug usage/ongoing psychotherapy. DISCUSSION: Considering that physician mental wellness is one of the most crucial determinants of healthcare quality, impaired mental wellness among future physicians is an obstacle to a well-functioning healthcare system. Our study targets researchers and authorities, who should focus on medical student mental wellness, and medical students themselves.

Natural language processing reveals research trends and topics in The Spine Journal over two decades: a topic modeling study.

BACKGROUND CONTEXT: The field of spine research is rapidly evolving, with new research topics continually emerging. Analyzing topics and trends in the literature can provide insights into the shifting research landscape. PURPOSE: This study aimed to elucidate prevalent and emerging research topics and trends within The Spine Journal using a natural language processing technique called topic modeling. METHODS: We utilized BERTopic, a topic modeling technique rooted in natural language processing (NLP), to examine articles from The Spine Journal. Through this approach, we discerned topics from distinct keyword clusters and representative documents that represented the main concepts of each topic. We then used linear regression models on these topic likelihoods to trace trends over time, pinpointing both "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. Additionally, we conducted an in-depth review of the trending topics in the present decade. RESULTS: Our analysis led to the categorization of 3358 documents into 30 distinct topics. These topics spanned a wide range of themes, with the most commonly identified topics being "Outcome Measures," "Scoliosis," and "Intradural Lesions." Throughout the history of the journal, the three hottest topics were "Degenerative Cervical Myelopathy," "Osteoporosis," and "Opioid Use." Conversely, the coldest topics were "Intradural Lesions," "Extradural Tumors," and "Vertebral Augmentation." Within the current decade, the hottest topics were "Screw Biomechanics," "Paraspinal Muscles," and "Biologics for Fusion," whereas the cold topics were "Intraoperative Blood Loss," "Construct Biomechanics," and "Material Science." CONCLUSIONS: This study accentuates the dynamic nature of spine research and the changing focus within the field. The insights gleaned from our analysis can steer future research directions, inform policy decisions, and spotlight emerging areas of interest. The implementation of NLP to synthesize and analyze vast amounts of academic literature exhibits the potential of advanced analytical techniques in comprehending the research landscape, setting a precedent for similar analyses across other medical disciplines.

Prognosis at Your Fingertips: A Machine Learning-Based Web Application for Outcome Prediction in Acute Traumatic Epidural Hematoma.

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.

The Predictive Abilities of Machine Learning Algorithms in Patients with Thoracolumbar Spinal Cord Injuries.

OBJECTIVES: The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury, while creating a publicly accessible online tool. METHODS: The American College of Surgeons Trauma Quality Program database was used to identify patients with thoracolumbar spinal cord injury. Feature selection was performed with the Least Absolute Shrinkage and Selection Operator algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. RESULTS: A total of 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharge with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, and LightGBM for major complications with an AUROC of 0.73. CONCLUSIONS: ML models demonstrate good predictive ability for in-hospital mortality and non-home discharge, fair predictive ability for major complications and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed.

Comparative Effectiveness of Open Versus Minimally Invasive Transforaminal Lumbar Interbody Fusion: An Umbrella Review of Meta-Analyses.

STUDY DESIGN: Umbrella review of meta-analyses. OBJECTIVE: To compile existing meta-analyses to provide analysis of the multiple postoperative outcomes in a comparison of open-transforaminal lumbar interbody fusions (O-TLIFs) versus minimally invasive transforaminal interbody fusions (MI-TLIFs). SUMMARY OF BACKGROUND DATA: TLIF is the standard surgical intervention for spinal fusion in degenerative spinal diseases. The comparative effectiveness of MI-TLIFs and O-TLIFs remains controversial. METHODS: A literature search was conducted in the PubMed, Scopus, and Web of Science databases. Titles and abstracts were initially screened, followed by a full-text review based on the inclusion criteria. Twenty articles were deemed eligible for the umbrella review. Data extraction and quality assessment using A Measurement Tool to Assess Systematic Reviews were performed. Effect sizes of the outcomes of interest from primary studies included in the meta-analyses were repooled. Repooling and stratification of the credibility of the evidence were performed using the R package metaumbrella . The pooled effect sizes were compared and interpreted using equivalent Hedges' g values. RESULTS: When the meta-analyses were pooled, MI-TLIF was found to have a shorter length of stay, less blood loss, and a higher radiation exposure time, with a highly suggestive level of evidence. Data regarding less postoperative drainage, infections, and Oswestry disability index for MI-TLIF were supported by weak evidence. Conversely, data regarding other postoperative outcomes were nonsignificant to draw any conclusions. CONCLUSION: Our umbrella review provides a comprehensive overview of the relevant strengths and weaknesses of each surgical technique. This overview revealed that MI-TLIF had better outcomes in terms of length of stay, blood loss, postoperative drainage, infections, and Oswestry disability index when compared with those of O-TLIF. However, O-TLIF had a better outcome for radiation exposure when compared with MI-TLIF.

Personalized Prognosis with Machine Learning Models for Predicting In-Hospital Outcomes Following Intracranial Meningioma Resections.

BACKGROUND: Meningiomas display diverse biological traits and clinical behaviors, complicating patient outcome prediction. This heterogeneity, along with varying prognoses, underscores the need for a precise, personalized evaluation of postoperative outcomes. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent intracranial meningioma resections from 2014 to 2020. We focused on 5 outcomes: prolonged LOS, nonhome discharges, 30-day readmissions, unplanned reoperations, and major complications. Six machine learning algorithms, including TabPFN, TabNet, XGBoost, LightGBM, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations were used to evaluate the importance of predictor variables. RESULTS: Our analysis included 7000 patients. Of these patients, 1658 (23.7%) had prolonged LOS, 1266 (18.1%) had nonhome discharges, 573 (8.2%) had 30-day readmission, 253 (3.6%) had unplanned reoperation, and 888 (12.7%) had major complications. Performance evaluation indicated that the top-performing models for each outcome were the models built with LightGBM and Random Forest algorithms. The LightGBM models yielded AUROCs of 0.842 and 0.846 in predicting prolonged LOS and nonhome discharges, respectively. The Random Forest models yielded AUROCs of 0.717, 0.76, and 0.805 in predicting 30-day readmissions, unplanned reoperations, and major complications, respectively. CONCLUSIONS: The study successfully demonstrated the potential of machine learning models in predicting short-term adverse postoperative outcomes after meningioma resections. This approach represents a significant step forward in personalizing the information provided to meningioma patients.

Predicting 30-Day Non-Seizure Outcomes Following Temporal Lobectomy with Personalized Machine Learning Models.

BACKGROUND: Temporal lobe epilepsy is the most common reason behind drug-resistant seizures and temporal lobectomy (TL) is performed after all other efforts have been taken for a Temporal lobe epilepsy. Our study aims to develop multiple machine learning (ML) models capable of predicting postoperative outcomes following TL surgery. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent TL surgery. We focused on 3 outcomes: prolonged length of stay (LOS), nonhome discharges, and 30-day readmissions. Six ML algorithms, TabPFN, XGBoost, LightGBM, Support Vector Machine, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations was used to evaluate importance of predictor variables. RESULTS: Our analysis included 423 patients. Of these patients, 111 (26.2%) experienced prolonged LOS, 33 (7.8%) had nonhome discharges, and 29 (6.9%) encountered 30-day readmissions. The top-performing models for each outcome were those built with the Random Forest algorithm. The Random Forest models yielded AUROCs of 0.868, 0.804, and 0.742 in predicting prolonged LOS, nonhome discharges, and 30-day readmissions, respectively. CONCLUSIONS: Our study uses ML to forecast adverse postoperative outcomes following TL. We developed accessible predictive models that enhance prognosis prediction for TL surgery. Making ML models available for this purpose represents a significant advancement in shifting toward a more patient-centric, data-driven paradigm.

From Text to Insight: A Natural Language Processing-Based Analysis of Topics and Trends in Neurosurgery.

BACKGROUND AND OBJECTIVES: Neurosurgical research is a rapidly evolving field, with new research topics emerging continually. To provide a clearer understanding of the evolving research landscape, our study aimed to identify and analyze the prevalent research topics and trends in Neurosurgery. METHODS: We used BERTopic, an advanced natural language processing-based topic modeling approach, to analyze papers published in the journal Neurosurgery . Using this method, topics were identified based on unique sets of keywords that encapsulated the core themes of each article. Linear regression models were then trained on the topic probabilities to identify trends over time, allowing us to identify "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. We also performed a focused analysis of the trends in the current decade. RESULTS: Our analysis led to the categorization of 12 438 documents into 49 distinct topics. The topics covered a wide range of themes, with the most commonly identified topics being "Spinal Neurosurgery" and "Treatment of Cerebral Ischemia." The hottest topics of the current decade were "Peripheral Nerve Surgery," "Unruptured Aneurysms," and "Endovascular Treatments" while the cold topics were "Chiari Malformations," "Thromboembolism Prophylaxis," and "Infections." CONCLUSION: Our study underscores the dynamic nature of neurosurgical research and the evolving focus of the field. The insights derived from the analysis can guide future research directions, inform policy decisions, and identify emerging areas of interest. The use of natural language processing in synthesizing and analyzing large volumes of academic literature demonstrates the potential of advanced analytical techniques in understanding the research landscape, paving the way for similar analyses across other medical disciplines.

Chondrosarcoma of the Mobile Spine in the Elderly: A National Cancer Database Study.

BACKGROUND: The current research on geriatric patients with spinal chondrosarcoma is limited. This study aimed to investigate the demographics, patterns of care, and survival of geriatric patients with chondrosarcoma of the mobile spine. METHODS: The National Cancer Database was queried from 2008 to 2018 for geriatric patients (60-89 years) with chondrosarcoma of the mobile spine. The primary outcome of this study was overall survival (OS). The secondary outcome was treatment utilization patterns. Survival analyses were conducted using log-rank tests and Cox proportional hazards regressions. Logistic regression models were utilized to assess correlations between baseline variables and treatment utilization. RESULTS: The database retrieved 122 patients. While 43.7% of the patients presented with tumors exceeding 5cm in size, the incidence of regional lymph node involvement or distant metastases was relatively low, affecting only 5% of the patients. Furthermore, 22.3% of the patients had tumors graded as 3-4. The five-year OS rate was 52.9% (95% confidence interval 42-66.6). The mortality risk was significantly associated with age, tumor grade and stage, and treatment plan. Most patients (79.5%) underwent surgery, while 35.9% and 4.2% were treated with radiotherapy and chemotherapy, respectively. Age, race, comorbidities, geographical region, tumor stage, and healthcare facility type significantly correlated with treatment utilization. CONCLUSION: Surgical resection significantly lowered the mortality risk in geriatric patients with spinal chondrosarcomas. Demographic and geographical factors significantly dictated treatment plans. Further studies are required to assess the role of radiotherapy and chemotherapy in treating these patients in the modern era.