Background: Endoscopic surgery has shown promise in treating Spontaneous Intracerebral Hemorrhage (sICH), but its adoption in county-level hospitals has been hindered by the high level of surgical expertise required.
Methods: In this retrospective study at a county hospital, we utilized a Cumulative Sum (CUSUM) control chart to visualize the learning curve for two neurosurgeons. We compared patient outcomes in the learning and proficient phases, and compared them with expected outcomes based on ICH score and ICH functional outcome score, respectively.
Results: The learning curve peaked at the 12th case for NS1 and the 8th case for NS2, signifying the transition to the proficient stage. This stage saw reductions in operation time, blood loss, rates of evacuation < 90 %, rebleeding rates, intensive care unit stay, hospital stay, and overall costs for both neurosurgeons. In the learning stage, 6 deaths occurred within 30 days, less than the 10.66 predicted by the ICH score. In the proficient stage, 3 deaths occurred, less than the 15.88 predicted. In intermediate and high-risk patients by the ICH functional outcome score, the proficient stage had fewer patients with an mRS ≥ 3 at three months than the learning stage (23.8 % vs. 69.2 %, P = 0.024; 40 % vs. 80 %, P = 0.360). Micromanipulating bipolar precision hemostasis and aspiration devices in the endoport's channels sped up the transition from learning to proficient.
Conclusion: The data shows a learning curve, with better surgical outcomes as surgeons gain proficiency. This suggests cost benefits of surgical proficiency and the need for ongoing surgical education and training in county hospitals.
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http://dx.doi.org/10.1016/j.jocn.2024.04.008 | DOI Listing |
J Med Internet Res
January 2025
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
PLoS One
January 2025
School of Exercise and Health, Shenyang Sport University, Shenyang, China.
Balance is crucial for various athletic tasks, and accurately assessing balance ability among elite athletes using simple and accessible measurement methods is a significant challenge in sports science. A common approach to balance assessment involves recording center of pressure (CoP) displacements using force platforms, with various indicators proposed to distinguish subtle balance differences. However, these indicators have not reached a consensus, and it remains unclear whether these analyses alone can fully explain the complex interactions of postural control.
View Article and Find Full Text PDFPLoS One
January 2025
Shanghai Xinhao Information Technology Co., Ltd., Shanghai, China.
Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. In this study, we used multi-source heterogeneous network information to build a network model, learn the network topology through multiple network diffusion algorithms, and obtain compressed low-dimensional feature vectors for predicting drug-target interactions (DTIs). We applied the metropolis-hasting random walk (MHRW) algorithm to improve the performance of the random walk with restart (RWR) algorithm, forming the basis by which the self-loop probability of the current node is removed.
View Article and Find Full Text PDFDetecting low birth weight is crucial for early identification of at-risk pregnancies which are associated with significant neonatal and maternal morbidity and mortality risks. This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios.
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