Rajendra Institute of Medical Sciences

Traumatic brain injury (TBI) is considered a major cause of death globally, resulting from trauma. Decompressive craniectomy (DC) may improve functional outcomes in patients with TBI and its associated complications. This study was designed to determine safety and efficacy of DC in improving clinical outcomes in TBI patients compared to standard therapy.

A systematic search was conducted across six electronic databases to identify relevant randomized controlled trials (RCTs) examining decompressive craniotomy (DC) and traumatic brain injury (TBI) from database inception until March 2021. The pooled risk ratio was estimated for categorical outcomes, while the pooled standardized mean difference with a 95% confidence interval was calculated for continuous outcomes. Statistical analysis software, including RevMan 5.4 and STATA version 17, was employed to perform this meta-analysis. The protocol for this study is registered with the OSF registry, ensuring transparency and reproducibility.

A total of 656 studies were screened, and five RCTs involving 665 subjects (334 in the DC group and 331 in the control group) were included in this meta-analysis. Our meta-analysis revealed a non-significant trend towards a higher rate of favorable clinical outcomes in subjects who underwent DC compared to those in the medical treatment (MT) group (risk ratio (RR) 1.20, 95% confidence interval (CI) 0.70 to 2.08, P = 0.50). In contrast, the mortality rate was significantly lower in patients treated with DC compared to those receiving MT alone (RR 0.58, 95% CI 0.47 to 0.73, P < 0.001). Additionally, intracranial pressure (ICP) levels were significantly lower in subjects who underwent DC compared to those receiving MT alone (standardized mean difference (SMD): - 0.87, 95% CI - 1.58 to - 0.16, P = 0.02). Furthermore, there was a statistically significant reduction in the duration of stay in the DC group compared to the MT alone group (SMD: - 1.18, 95% CI - 1.49 to - 0.86, P < 0.001).

This study presents evidence suggesting that DC is linked to a lower mortality rate, decreased ICP, and shorter hospital stays among patients with moderate to severe TBI. However, it did not show a significant impact on improving favorable clinical outcomes.

Dengue fever (DF) is a serious public health concern, especially in tropical regions like India. Cases cluster during the monsoon season due to the proliferation of Aedes mosquitoes, significantly burdening public health and the economy. In addition to prevention and control, identifying risk factors that provide early indications of progression to severe forms of the disease can help in prognostication and improve case management. This study aims to explore the association between ABO blood groups and the severity of dengue disease in pediatric patients admitted to a tertiary care hospital in Jharkhand.

This hospital-based observational cross-sectional study enrolled 88 patients, aged 1 to 18 years, with laboratory-confirmed dengue. Clinical features, hematological parameters, and outcomes, including DF, dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS), were analyzed across different blood groups.

A higher prevalence of dengue was noted in patients with blood group O (33, 37.5%) compared to other blood groups. While blood group O was associated with higher rates of severe outcomes such as DHF in 12 (46.1%) patients and DSS in 9 (56.2%) patients, statistical analysis did not establish a significant association between ABO blood groups and dengue severity (P = 0.155). Common presenting symptoms included abdominal pain in 61 (69.3%), body ache in 57 (64.8%), and fever in 50 (56.8%) patients, while complications like vascular leakage and thrombocytopenia varied across blood groups.

While no definitive link between blood group and disease progression was found, the result of higher prevalence of severe disease in blood group O can help us to stay vigilant. Identifying predictive markers for severe dengue can enhance handling of these patients and improve morbidity and mortality in pediatric populations. This study underscores the need for further research into the genetic and immunological factors that influence dengue severity.

Introduction Artificial intelligence (AI) is revolutionizing healthcare, offering opportunities to improve diagnosis, clinical care, and medical education. Despite its growing importance, familiarity with AI in medical education remains limited, necessitating a deeper understanding of perceptions among medical students and faculty. The aim of the study is to explore the perceptions of medical students and faculty regarding the use of AI in medical education and its implications for curriculum improvement. Materials and methods A cross-sectional study was conducted over six months (January-June 2024) at Sheikh Bhikhari Medical College, Hazaribagh, India. An online questionnaire was distributed to 299 participants, including 242 (80.93%) students, 20 (6.68%) residents, and 37 (12.04%) faculty members, using convenience sampling. Data was analyzed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, NY, USA). Results The results revealed that 260 (86.95%) understood AI concepts, but only 36 (12.04%) were very familiar with its application in education. Additionally, 260 (87%) supported AI integration into medical curricula, and 273 (91.3%) believed it could improve educational efficiency. However, 179 (59.9%) had no prior experience with AI tools. Participants highlighted AI's potential in diagnostics (154, or 51.5%), clinical reasoning (51, or 17.1%), radiology (50, or 16.7%), pathology (31, or 10.4%), and 265 (88.62%) expressed a desire for structured AI training. Discussion While enthusiasm for AI integration is evident, gaps in exposure and structured education persist. Similar findings in global studies underline the urgent need for standardized curricula and faculty training. Conclusion This study highlights the importance of incorporating AI in medical education to prepare healthcare professionals for future challenges. Addressing gaps in knowledge and providing practical exposure are crucial for leveraging AI's full potential in medicine. Further multi-center studies are recommended to validate these findings.