Background: Medical students are known to be subjected to immense stress under competitive curricula and have a high risk of depression, burnout, anxiety and sleep disorders. There is a global trend of switching from norm-referenced assessment (NRA) to criterion-referenced assessment (CRA), and these changes may have influenced the quality of life (QOL), sleep phase, sleep quality, stress, burnout, and depression of the medical students. We hypothesized that there is a significant difference of QOL between CRA and NRA and that sleep, stress, burnout, and depression are the main contributors. Methods: By administering an online survey regarding QOL and its contributors to Korean medical students, 365 responses from 10 medical schools were recorded. To clarify the complex relationship between the multiple factors in play, we applied nonlinear machine learning algorithms and utilized causal structure learning techniques on the survey data. Results: Students with CRA had lower scores in stress (68.16 ± 11.29, 76.03 ± 12.38, P< 0.001), burnout (48.09 ± 11.23, 55.93 ± 13.07, P < 0.001), depression (12.77 ± 9.82, 16.44 ± 11.27, P = 0.003) and higher scores in QOL (95.79 ± 16.20, 89.65 ± 16.28, P < 0.001) compared with students with NRA. Multiple linear regression, permutation importance of the random forest model and the causal structure model showed that depression, stress and burnout are the most influential factors of QOL of medical students. Conclusion Medical students from schools that use CRA showed higher QOL scores, as well as lower burnout, stress and depression when compared with students from schools that use NRA. These results may be used as a basis for granting justification for the transition to CRA.

For personalized drug dosing, prediction models may be utilized to overcome the inter-individual variability. Multiple linear regression has been used as a conventional method to model the relationship between patient features and optimal drug dose. However, linear regression cannot capture non-linear relationships and may be adversely affected by non-normal distribution and collinearity of data. To overcome this hurdle, machine learning models have been extensively adapted in drug dose prediction. In this tutorial, random forest and neural network models will be trained in tandem with a multiple linear regression model on the International Warfarin Pharmacogenetics Consortium dataset using the scikit-learn python library. Subsequent model analyses including performance comparison, permutation feature importance computation and partial dependence plotting will be demonstrated. The basic methods of model training and analysis discussed in this article may be implemented in drug dose-related studies.

Clinical trials are essential for medical research, but they often face challenges in matching patients to trials and planning. Large language models (LLMs) offer a promising solution, signaling a transformative shift in the field of clinical trials. This review explores the multifaceted applications of LLMs within clinical trials, focusing on five main areas expected to be implemented in the near future: enhancing patient-trial matching, streamlining clinical trial planning, analyzing free text narratives for coding and classification, assisting in technical writing tasks, and providing cognizant consent via LLM-powered chatbots. While the application of LLMs is promising, it poses challenges such as accuracy validation and legal concerns. The convergence of LLMs with clinical trials has the potential to revolutionize the efficiency of clinical trials, paving the way for innovative methodologies and enhancing patient engagement. However, this development requires careful consideration and investment to overcome potential hurdles.

Appropriate prescription writing is one of the critical medical processes affecting the quality of public health care. However, this is a complex task for newly qualified intern doctors because of its complex characteristics requiring sufficient knowledge of medications and principles of clinical pharmacology, skills of diagnosis and communication, and critical judgment. This study aims to gather data on the current status of undergraduate prescribing education in South Korea. Two surveys were administered in this study: survey A to 26 medical schools in South Korea to gather information on the status of undergraduate education in clinical pharmacology; and survey B to 244 intern doctors in large hospitals to gather their opinions regarding prescribing education and ability. In survey A, half of the responding institutions provided prescribing education via various formats of classes over two curriculums including lecture, applied practice, group discussions, computer-utilized training, and workshops. In survey B, we found that intern doctors have the least confidence when prescribing drugs for special patient populations, especially pregnant women. These intern doctors believed that a case-based practical training or group discussion class would be an effective approach to supplement their prescribing education concurrently or after the clerkship in medical schools or right before starting intern training with a core drug list. The results of the present study may help instructors in charge of prescribing education when communicating and cooperating with each other to improve undergraduate prescribing education and the quality of national medical care.
Curriculum ; Diagnosis ; Education ; Education, Medical ; Female ; Group Practice ; Humans ; Judgment ; Korea ; Pharmacology, Clinical ; Pregnant Women ; Prescriptions ; Public Health ; Schools, Medical ; Writing

Dehydroevodiamine.HCl (DHED) has been reported to prevent memory impairment and neuronal cell loss in a rat model with cognitive disturbance. We investigated the effect of DHED on memory impairment and behavioral abnormality caused by stress. We demonstrated that DHED can improve stress-induced memory impairments and depression-like behaviors by using open-field test, Y-maze test and forced swimming test. DHED treatment significantly recovered the decreases in the levels of neural cell adhesion molecule (NCAM) proteins caused by stress and the decreases in cell viability. Our results suggested that DHED is a potential drug candidate for neuronal death, memory impairment and depression induced by stress.
Animals ; Cell Survival ; Depression* ; Fluoxetine ; Memory* ; Models, Animal ; Neural Cell Adhesion Molecules ; Neurons ; Physical Exertion ; Rats*