Coordinates: 28°37′33″N 77°11′54″E / 28.6257591°N 77.1984509°E / 28.6257591; 77.1984509Dr. Ram Manohar Lohia Hospital (formerly known as Willingdon Hospital) is a government hospital in New Delhi, India.The hospital was founded, with 54 beds, in 1932 by the British Raj for their own government staff. In 1954, in the newly independent India, control of the hospital was transferred to the Central Government's Ministry of Health and Family Welfare. It was renamed in 1970s after Dr. Ram Manohar Lohia, the most important proponent of socialist ideology in India.The hospital is spread over 30 acres (12 ha), with 4 acres (1.6 ha) of land set aside for its Nurse's Hostel. It is a Central Government hospitals because of its well-positioned location, a seventy-one bed Nursing Home for Central Government Health Scheme beneficiaries, and extensive subspecialty care. The hospital also has emergency services and has current[when?] plans for the construction of a 16-Storey Doctor's Hostel and a new MBBS building on its free land.Annually,[when?] the hospital provides services to about 1.2 million patients as OPD cases, admits about 46,000 patients and attends about 150,000 emergency patients; it has 1420 beds. The hospital conducts about 10,000 CT scans, 2,000 MRI scans, 200,000 X-ray cases, 2.8 million laboratory tests, 25,000 ultrasound scans, and about 9,000 major and 40,000 minor operations per year. The hospital runs daily separate CGHS OPDs for CGHS beneficiaries.The hospital started MBBS course from 2019 session with 100 seats under the aegis of Atal Bihari Vajpayee Institute of Medical Sciences.The Hospital also has plans to increase the number of beds to about 3000 exceeding that of Safdarjung Hospital..
Bowen's Disease (BD), recognised as Squamous Cell Carcinoma (SCC) in situ, typically affects older individuals and postmenopausal women, whereas Merkel Cell Carcinoma (MCC) is an aggressive neuroendocrine skin malignancy with a predilection for elderly white males. This case report delves into the unique convergence of BD and MCC in a 45-year-old woman, presenting as BD in the left breast and arm, coupled with MCC in the left wrist with metastasis to the left axilla. The patient was treated with wide local excision of the breast and arm lesions, coupled with en masse clearance of the left axilla due to metastatic MCC. This unique case underscores the intricate interplay between different cutaneous malignancies, emphasising the need for continued research to unravel the underlying mechanisms and optimise therapeutic strategies in rare and challenging dermatological presentations.
Obesity is a global health crisis affecting developing nations, including India. The management of obesity continues to evolve with newer drugs, metabolic and bariatric surgery and endoscopic interventions, requiring family physicians and specialists to adapt their clinical practice accordingly. There is an urgent need for a standardized algorithm to diagnose, stage, and treat obesity. The Endocrine Society of India (ESI) and the Obesity Surgeons Society of India (OSSI) appointed a steering committee to develop an evidence-based algorithm for managing patients with obesity in India. This was put to vote by 80 specialists (38 from OSSI and 42 from ESI) in a physical meeting. A proposed stage-wise algorithm based on Edmonton Obesity Staging System, Asian definition of obesity, and resources in India, received 100
Artificial intelligence (AI) is increasingly shaping orthopedic practice, including diagnostics, surgical planning, postoperative monitoring, and education. Despite its growth, many surgeons and residents remain unfamiliar with AI concepts, applications, and strategies for responsible integration, including explainability, fairness, and uncertainty-aware AI. This narrative review synthesizes current literature on AI in orthopedics, covering terminology, key technologies (machine learning, deep learning, convolutional neural networks, natural language processing, computer vision, robotics, pattern recognition, predictive analytics, explainable AI, fairness, and uncertainty-aware AI), clinical applications, and educational implications. AI enhances care across the orthopedic continuum. In diagnostics, AI-assisted imaging improves fracture detection, tumor and infection identification, and implant recognition. Preoperative planning and surgical navigation benefit from the use of automated templating, 3D planning, and robotic systems. Postoperative monitoring utilizes wearable devices, sensors, and predictive analytics to track recovery and identify potential complications. Educational applications include video-based skill assessment, VR simulation with adaptive feedback, and personalized learning analytics. Ethical and practical considerations, such as accountability, data privacy, regulatory compliance, transparency, and bias mitigation, are crucial for the safe adoption of AI. Structured AI literacy and hands-on training are recommended to prepare residents for effective integration. AI can augment decision-making, enhance surgical precision, and improve resident education. Safe and responsible adoption requires foundational AI knowledge, critical appraisal of tools, and integration as a complementary resource while retaining ultimate clinical judgment. This review provides a framework for understanding AI terminology, applications, and strategies for the responsible incorporation of AI into orthopedic practice and training.
Background Post-traumatic epilepsy (PTE) is a major cause of acquired epilepsy. Early post-traumatic seizures (EPTS), occurring within the first seven days following traumatic brain injury (TBI), are known to worsen secondary brain damage and adversely affect patient outcomes. Despite the recognized burden, the incidence and risk factors of EPTS in the Indian population remain poorly characterized. Objective To determine the incidence of EPTS and identify the associated demographic, clinical, and radiological risk factors in patients presenting to a tertiary care neurosurgical institute. Methods This prospective observational study was conducted over two years (November 2019-October 2021) at the Department of Neurosurgery of a tertiary care teaching institute in India. A total of 1,035 consecutive patients with head injuries who presented to the emergency room were enrolled. Patients were started on levetiracetam 500 mg 12 hourly in adults and 20mg/kg 12 hourly in pediatric patients, in oral or intravenous (I.V.) route as per clinical assessment. The decision to start antiepileptics was clinical; not all patients received antiepileptics. However, a positive CT finding, prior history of seizures, or a Glasgow Coma Scale (GCS) of less than 13 were started on anti-epileptics as per the institute protocol. Patients were monitored for clinical seizure activity during the first seven days post-injury or until hospital discharge, whichever occurred first. Patients were classified according to the GCS score, mechanism of injury, CT findings, age, and sex. Results EPTS occurred in 29 (2.8%) of the 1,035 patients. Seizure incidence was highest in the severe TBI group (GCS 3-6: 5 (35.9%)), followed by moderate TBI (GCS 7-12: 11 (9.0%)), and mild TBI (GCS 13-15: 13 (1.5%)). Falls were the most common mechanism, with the highest seizure rate (15, 4.09%). The CT findings associated with the highest EPTS risk included brainstem contusion (1, 50%), subdural hemorrhage (8, 17.7%), and hemorrhagic contusion (7, 10%). The 0-10 age group had the highest absolute number of seizures (13, 6.2%). Male patients accounted for 76.7% (n=22) of the EPTS cases. Conclusions The incidence of EPTS in this study was 2.8%. The severity of TBI (p <0.001), a positive hemorrhagic finding in CT scan (p <0.001), and a younger age group (0-10 years) (p = 0.036) were statistically significantly associated with EPTS. These findings emphasize the need for targeted seizure surveillance and prophylaxis in high-risk TBI subgroups.
