Aggressive lumbar osteoblastoma with secondary aneurysmal bone cysts is rare and may involve both posterior elements and vertebral bodies. When extensive multilevel involvement is present, a combined posterior and transpsoas approach can facilitate complete tumor resection, restore spinal stability, and achieve favorable neurological and functional outcomes with low recurrence risk.
Background: The global consensus was established on newborn hearing screening for early identification and management of hearing loss, which is important in minimizing and preventing the negative impact of hearing loss in pediatric population. Aim and Objectives: This study was planned to assess the impact of universal neonatal hearing screening programme (UNHSP) on age of implantation, time takes for the process and candidacy evaluation for cochlear implantation (CI) in children with bilateral congenital profound sensorineural hearing loss (SNHL), who underwent CI at a tertiary care teaching hospital of India. Material and Methods: Children with congenital severe to profound SNHL, who underwent CI after thorough evaluation were included in the study. The date of birth (DOB), date of first hearing screening, date of registration and/or enrollment at the center for further workup and reporting to the CI candidacy committee, date of CI surgery was reviewed retrospectively. Results: A total of 250 children who underwent cochlear implantation at a tertiary care center were included in this study. Out of which, approximately 68% children were screened for hearing loss (HL) through UNHSP, rest of the 32% were not screened for HL during birth due to several reasons. The difference between both the groups with respect to the age of diagnosis of HL and age of cochlear implantation is significant. However, the time taken for the evaluation process was not substantial between both the groups. Conclusion: The implementation of UNHSP has several positive impacts in respect to the age at diagnosis of HL, age at the time of receiving CI and the overall growth of the recipient. So, the UNHSP has several positive impacts with respect to the age at diagnosis of HL, age at the time of receiving CI and the overall growth in children with congenital severe to profound SNHL and the proper execution of this programme is the need of the hour.
Aim and Objectives This study investigates the impact of Acceptable Noise Level (ANL) on speech perception and evaluates how varying degrees of sensorineural hearing loss (SNHL) influence a user’s subjective tolerance for background noise. The primary objective was to determine the relationship between ANL thresholds and clinical success in both normal-hearing and hearing-impaired populations. Material and Methods A total of 72 subjects were recruited and divided into two cohorts: Group 1 (n = 24, normal hearing) and Group 2 (n = 48, varying degrees of SNHL ranging from mild to moderately severe). Following comprehensive audiometric and impedance evaluations, behavioral ANL was measured using standardized Hindi and English speech passages. This approach allowed for a comparative analysis of how the severity of hearing loss dictates the psychoacoustic willingness to accept background noise while maintaining focus on a primary speech signal. Results The data revealed a clear progression: as the severity of hearing loss increased, the capacity to tolerate background noise significantly decreased. The mean ANL for the normal-hearing group was 11.12 ± 2.29 dB. Within the hearing-impaired cohort, mean ANL values rose progressively with the degree of loss: 12.15 ± 1.28 dB (mild), 16.20 ± 2.7 dB (moderate), and 18.60 ± 2.61 dB (moderately severe). These findings indicate that higher degrees of SNHL are associated with a diminished ability to manage noise interference, regardless of absolute word recognition scores. Conclusion The results underscore ANL as a critical prognostic indicator for hearing aid outcomes. While individuals with low ANLs ( < 7 dB) demonstrate high tolerance and a strong probability of device success, those with high ANLs ( > 13 dB) face significant challenges in noise acceptance. Incorporating ANL testing into the standard audiological battery enables clinicians to provide more accurate counseling, set realistic expectations, and personalize rehabilitative strategies for hearing aid users.
Introduction: Queuing theory, originating with Agner Krarup Erlang in 1908, has evolved to address healthcare operations, notably in managing outpatient appointments and hospital services. Its application helps hospitals optimize service capacity, reduce delays, and predict congestion, ultimately improving patient care quality, staff satisfaction, and operational efficiency. Aim and Objectives: This study aimed to optimize the waiting time at the Blood Sample Collection Center (BSCC) by studying process flow, the arrival and service pattern, and factors affecting waiting time, and suggesting measures to optimize the same by applying queuing theory. Methodology: A cross-sectional study at the BSCC of a tertiary care teaching hospital. The study used a mixed-method approach, gathering data through the study of patient records, direct observation at the BSCC, and unstructured interviews with key stakeholders. Observations included queue length, arrival patterns, and service times were applied to assess workload distribution and server utilization across five-time slots. M/M/s Queuing Formula Spread Sheet Excel template. Results: The study analyzed a 1082-bed tertiary care hospital’s BSCC. The process was divided into two phases: registration and blood sample collection. The average waiting time was higher for females. The average utilization factor for Phase I was 0.74 and for Phase II was 0.57. Conclusion: Implementing priority queuing at BSCC can reduce congestion, improve service efficiency, and enhance patient satisfaction. Modifications such as dedicated counters for specific cases and addressing physical facilities can help manage peak-hour demand, reduce waiting times, and optimize the number of patients attending daily.
Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for expert systems that enable continuous and interpretable cardiac monitoring. We present ArmFormer, a knowledge-driven expert system that leverages Transformer-based reasoning for robust cardiac event detection from wearable armband electrocardiogram signals. The model integrates domain-guided multi-scale patch encoding to capture waveform morphology and rhythm dependencies, while local gated Transformer blocks enhance temporal continuity and suppress noise-induced variability. A lead-wise attention mechanism coupled with gradient-based visualisation provides interpretability by highlighting clinically relevant regions such as QRS complexes, P waves, and ST segments. On an in-house cohort of 99 subjects comprising 6211 normal and 10,030 abnormal 10-s segments, ArmFormer achieved 91.66% accuracy, 91.57% F1-score, 91.41% sensitivity, and 97.36% AUC under a subject-exclusive protocol that prevents patient-level information leakage. Compared with convolutional and residual baselines, AUC improved by up to 5%, while floating-point operations and parameters were reduced by 29-fold and 47-fold, respectively, achieving 2.58 ms inference latency per segment. External validation on CPSC2018 and Chapman showed accuracies of 83.65% and 94.69%, with AUCs of 96.69% and 99.38%, respectively. By combining domain-guided encoding, noise-robust temporal reasoning, and interpretable attention, ArmFormer provides a practical and reliable framework for expert-level cardiac event detection in wearable monitoring scenarios.
