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Introduction To present and discuss the clinical presentations, investigations, and treatment options for skull bone tumors. Materials and Methods This study was conducted from January 2019 to December 2019 at the Department of Neurosurgery. During this period, eight patients presented with skull bone tumor in the outpatient department. All patients were thoroughly investigated. Surgery was conducted on six patients and two patients had disseminated carcinoma; hence, surgery was not done. Patients were regularly followed-up after the surgery. Results In our study, out of eight cases, five were females and three were males. We had two cases of fibrous dysplasia, two cases of osteomas, and one case each of brown tumor, metastases from lung carcinoma, metastases from follicular carcinoma of thyroid, and Ewing sarcoma/primitive neuroectodermal tumor (PNET). Excision of tumor was performed where indicated and adjuvant chemo- and radiotherapy was suggested wherever required. Conclusion Bony tumors of the skull are uncommon diseases for the neurosurgeons. These tumors require a careful diagnosis with suitable radiological examinations and proper clinical correlation for proper management.

Increasing use of button battery (BB) in household products and toys is responsible for the growing incidence of button battery ingestion (BBI). The BBI may cause life‑threatening complications. We present a series of three cases of complicated BBI (lithium cell) with delayed presentation; one of them could not survive due to tracheoesophageal fistula and sepsis. Here, we highlight the importance of early endoscopic intervention and careful follow‑up in children with lithium battery ingestion.

Introduction Traumatic brain injury (TBI) is a global health issue, accounting for a significant number of adult and pediatric deaths and morbidity. Computed tomography (CT) is an important diagnostic modality for TBI. The primary goal of this study was to determine if there were any significant radiological differences in CT brain findings between adult and pediatric populations. Materials and Methods Data of individual patients were collected from admission to discharge/death, which included various parameters in terms of demographics, mechanism of injury, and patient outcome which were later analyzed. A total of 1,150 TBI patients were enrolled in the study. Results  The most common mode of injury in adults is road traffic accident (RTA) followed by fall from height (FFH), while in pediatrics it is vice versa. Findings of basal cisterns on CT brain were found to be statistically significant in both groups; 65% adults and 71% pediatrics had only one abnormal CT finding. Most common combination CT finding in adults was acute subdural hematoma (ASDH) and basal cistern abnormality, while in pediatrics it was traumatic subarachnoid hemorrhage (SAH) and contusion. Rotterdam score (based on CT brain findings) was significantly lower for pediatric age group compared with adults. It was 2.2 ± 0.85 for adults and 1.99 ± 0.74 for pediatrics, which was statistically significant (p < 0.001). Conclusions  The Rotterdam score has immense predictive power for prognostication of mortality status. Pediatric age group has better prognosis in terms of survival as compared with adults, thus justifying the role of Rotterdam CT score for mortality risk stratification in providing clinical care.

Context The aim of the study was to develop a prognostic model using artificial intelligence for patients undergoing lumbar spine surgery for degenerative spine disease for change in pain, functional status, and patient satisfaction based on preoperative variables included in following categories—sociodemographic, clinical, and radiological. Methods and Materials A prospective cohort of 180 patients with lumbar degenerative spine disease was included and divided into three classes of management—conservative, decompressive surgery, and decompression with fixation. Preoperative variables, change in outcome measures (visual analog scale—VAS, Modified Oswestry Disability Index—MODI, and Neurogenic Claudication Outcome Score—NCOS), and type of management were assessed using Machine Learning models. These were used for creating a predictive tool for deciding the type of management that a patient should undergo to achieve the best results. Multivariate logistic regression was also used to identify prognostic factors of significance. Results The area under the curve (AUC) was calculated from the receiver-operating characteristic (ROC) analysis to evaluate the discrimination capability of various machine learning models. Random Forest Classifier gave the best ROC-AUC score in all three classes (0.863 for VAS, 0.831 for MODI, and 0.869 for NCOS), and the macroaverage AUC score was found to be 0.842 suggesting moderate discriminatory power. A graphical user interface (GUI) tool was built using the machine learning algorithm thus defined to take input details of patients and predict change in outcome measures. Conclusion This study demonstrates that machine learning can be used as a tool to help tailor the decision-making process for a patient to achieve the best outcome. The GUI tool helps to incorporate the study results into active decision-making.