International Journal of Social Health

The Relationship Between Kinesthetic-Tactile Stimulation and Growth in Low Birth Weight Infants at Waled Regional General Hospital, Cirebon Regency

2025-08-27T03:21:04+00:00

Background: Low birth weight (LBW) is a newborn baby weighing less than 2500 grams regardless of gestational age. Based on WHO 2020 data, there are 19.8 million newborns with around 14.7% of all babies born globally in that year being LBW. LBW conditions cause the body's organs to not be able to function perfectly, so that the adjustment of organ function to changes in conditions from intrauterine to environmental conditions outside the womb is very difficult for babies. There are factors that can increase the weight of LBW babies, one of which is by means of Tactile Kinesthetic Stimulation (TKS). Aim: This study aims to analyze the corelation between the provision of tactile kinesthetic stimulation and the growth of low birth weight infants at Waled Genereal Hospital Cirebon District. Methods: This research uses an analytical observational method with a Cross Sectional design from secondary data in the form of medical records with a sample size of 60 babies obtained by total sampling. Data were tested with Spearman rho. Results: The results of the analysis showed a correlation between the provision of tactile kinesthetic stimulation on the weight growth of LBW babies (p-value 0.001) with r = 0.874. Conclusion: There is a correlation between the provision of tactile kinesthetic stimulation and the growth of low birth weight infants at Waled Genereal Hospital Cirebon District.

Characteristics of Musculoskeletal Injury Patients at Hanau Hospital, Central Kalimantan Province in 2024

2025-08-27T03:19:31+00:00

Musculoskeletal injuries are one of the leading causes of disability and death in the world, including in Indonesia, with a high prevalence in patients who come to the Emergency Department (IGD). This study aims to determine the characteristics of musculoskeletal injury patients in the emergency room of Hanau Hospital, Central Kalimantan Province in 2024. The study used a descriptive design with secondary data from the patient's medical records for the period January 1–December 31, 2024. The inclusion criteria include patients with a diagnosis of musculoskeletal injury and complete medical record data, while exclusion are patients without such a diagnosis or incomplete data. The analysis was carried out descriptively on the variables of age, gender, and type of injury according to location. The results showed a total of 123 musculoskeletal injury patients, with the highest age distribution in the 11–20 years group (20%), followed by the 21–30 years group (17%). Based on gender, men dominate by 70%, while women 30%. The most injured locations were clavicle fractures (8%), wrist and other hand fractures (9%), and distal radius fractures (6%). This pattern is in line with previous research that showed upper extremity fractures as the most common case in active populations. These findings confirm the need for prevention strategies based on safety education, improvement of rehabilitation facilities, and ergonomic interventions tailored to age groups and types of activities. This study provides an initial overview of the epidemiology of musculoskeletal injuries at Hanau Hospital which can be the basis for prevention and treatment policies in the Central Kalimantan region.

Classification of Glioma Grades on Diffusion-Weighted MRI Using Random Forest in Machine Learning

2025-08-27T03:20:36+00:00

Background: Glioma, a type of brain tumor, is classified into two grades, LGG and HGG, and requires early detection due to the high mortality rate from brain tumors in Indonesia, with 5,323 new cases and 4,229 deaths. For patients with kidney disorders, gadolinium-based contrast MRI is unsuitable, making DWI images and AI-based systems viable alternatives for detection.

Objective: This study aims to develop a machine learning (ML) model using the random forest (RF) algorithm for automatic glioma grade classification from DWI MRI Brain images and to evaluate its performance.

Method: Purposive sampling was used to collect 1,848 DWI MRI brain images. A quasi-experimental design with a post-test control group was employed. The model's validity was assessed by media experts using a Likert scale to evaluate aspects such as user interface design, system performance, flexibility, usability, and reliability. Reliability was tested using Fleiss' Kappa for inter-rater reliability.

Results: The ML model achieved 82% accuracy and a micro-AUC of 0.96, excelling in Normal and Grade 4 classifications but needing better recall for Grades 1 and 2. IT experts rated it positively: 84% for User Interface Design, 82% for Usability, 84.4% for System Performance, and 68.9% and 73.3% for Flexibility and Reliability. The Wilcoxon test found no significant differences from respondents (p > 0.05), with Fleiss' Kappa at 0.85 and 92% Observed Agreement.

Conclusion: This study successfully developed and tested an RF model for glioma classification, demonstrating consistent and accurate results.

A Comparative Assessment Of Insulin Efsitora Alfa And Insulin Icodec In Diabetes Mellitus Management: A Scooping Review

2025-08-27T03:18:39+00:00

Background: Diabetes Mellitus management continues to evolve with innovations in insulin therapy aimed at improving efficacy and patient comfort. Purpose: This study aims to compare the efficacy and safety of Insulin Efsitora Alfa and Insulin Icodec in the management of Diabetes Mellitus. Methods: This scoping review analyzed articles published between August 2020 and August 2025 from PubMed, ScienceDirect, and other sources. Inclusion criteria included English-language observational studies addressing the efficacy and safety of both insulins in DM. Ten articles were selected after a rigorous screening process. Results: Both insulins demonstrated non-inferior efficacy in lowering HbA1c compared to daily basal insulin, with pharmacokinetic/pharmacodynamic profiles favoring once-weekly dosing. General tolerability was good, and Time in Range (TIR) was comparable. However, Insulin Icodec was associated with a higher incidence of hypoglycemia, especially in type 1 diabetes, compared to Insulin Efsitora Alfa which showed similar hypoglycemia rates to daily insulin. Patient satisfaction with Icodec was higher in type 2 diabetes, but lower in type 1. Implications: Both insulins offer the potential to improve patient adherence. Insulin Efsitora Alfa may be more suitable for patients prone to hypoglycemia. The increased risk of hypoglycemia with Insulin Icodec requires careful clinical consideration and dose titration. Further head-to-head comparison studies are needed to guide optimal therapy.

Web-Based MRI Queue System Integrated with Modalities and Radiology Reports at Royal Taruma Hospital

2025-08-27T03:20:08+00:00

This research employs the Rapid Application Development (RAD) methodology, which focuses on fast and iterative system development. The research stages include needs analysis, system design using Business Process Model and Notation (BPMN), prototype development, testing, and system implementation. The evaluation involved expert validators and users, using a Likert scale to assess efficiency, effectiveness, security, and satisfaction. The results indicate that the web-based queuing system successfully reduced patient waiting times by up to 30%, improved scheduling accuracy, and minimized manual errors. The system’s implementation also accelerated radiology expertise processes through RIS and MRI modality integration, thereby enhancing operational efficiency and user satisfaction. System validation achieved a reliability level of 100%, with an average user satisfaction score of 4.50 on a scale of 5.