The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

Objectives: This study aimed to identify trends in avoidable mortality (AVM) in 16 provincial and metropolitan regions of Korea and determine the factors influencing AVM. Methods: First, the avoidable mortality rate (AVMR) was calculated using the Statistics Korea cause-of-death and population data by age and region from 2013 to 2022. Second, a health determinants model was built, and we identified the factors influencing AVM using generalized estimating equations analysis. Results: Although the AVMR per 100 000 people displayed a steadily decreasing trend from 2013 to 2020, it began to increase in 2021. Meanwhile, Jeonnam, Jeonbuk, Gyeongnam, Gyeongbuk, Chungnam, Chungbuk, and Gangwon Provinces showed a higher AVMR than the national average. The analysis revealed that each 1-unit increase in the older adult population, smoking, perceived stress, or non-local medical utilization was associated with an increase in the AVMR. Conversely, 1-unit increases in the male-to-female ratio, marriage rate, positive self-rated health, local medical utilization, doctor population, influenza vaccination rate, cancer screening rate, or financial independence were associated with decrease in the AVMR. Conclusions This study established that the AVMR, which had been continuously decreasing across the 16 regions, shifted to an increasing trend in 2021. We also identified several factors influencing AVM. Further studies are needed to confirm the reasons for this shift in the AVMR and explore the factors that influence AVM across Korea’s 16 provincial and metropolitan regions.

Objectives: This study aimed to identify trends in avoidable mortality (AVM) in 16 provincial and metropolitan regions of Korea and determine the factors influencing AVM. Methods: First, the avoidable mortality rate (AVMR) was calculated using the Statistics Korea cause-of-death and population data by age and region from 2013 to 2022. Second, a health determinants model was built, and we identified the factors influencing AVM using generalized estimating equations analysis. Results: Although the AVMR per 100 000 people displayed a steadily decreasing trend from 2013 to 2020, it began to increase in 2021. Meanwhile, Jeonnam, Jeonbuk, Gyeongnam, Gyeongbuk, Chungnam, Chungbuk, and Gangwon Provinces showed a higher AVMR than the national average. The analysis revealed that each 1-unit increase in the older adult population, smoking, perceived stress, or non-local medical utilization was associated with an increase in the AVMR. Conversely, 1-unit increases in the male-to-female ratio, marriage rate, positive self-rated health, local medical utilization, doctor population, influenza vaccination rate, cancer screening rate, or financial independence were associated with decrease in the AVMR. Conclusions This study established that the AVMR, which had been continuously decreasing across the 16 regions, shifted to an increasing trend in 2021. We also identified several factors influencing AVM. Further studies are needed to confirm the reasons for this shift in the AVMR and explore the factors that influence AVM across Korea’s 16 provincial and metropolitan regions.

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

Objectives: This study aimed to identify trends in avoidable mortality (AVM) in 16 provincial and metropolitan regions of Korea and determine the factors influencing AVM. Methods: First, the avoidable mortality rate (AVMR) was calculated using the Statistics Korea cause-of-death and population data by age and region from 2013 to 2022. Second, a health determinants model was built, and we identified the factors influencing AVM using generalized estimating equations analysis. Results: Although the AVMR per 100 000 people displayed a steadily decreasing trend from 2013 to 2020, it began to increase in 2021. Meanwhile, Jeonnam, Jeonbuk, Gyeongnam, Gyeongbuk, Chungnam, Chungbuk, and Gangwon Provinces showed a higher AVMR than the national average. The analysis revealed that each 1-unit increase in the older adult population, smoking, perceived stress, or non-local medical utilization was associated with an increase in the AVMR. Conversely, 1-unit increases in the male-to-female ratio, marriage rate, positive self-rated health, local medical utilization, doctor population, influenza vaccination rate, cancer screening rate, or financial independence were associated with decrease in the AVMR. Conclusions This study established that the AVMR, which had been continuously decreasing across the 16 regions, shifted to an increasing trend in 2021. We also identified several factors influencing AVM. Further studies are needed to confirm the reasons for this shift in the AVMR and explore the factors that influence AVM across Korea’s 16 provincial and metropolitan regions.

Background: People with special needs tend to require diverse behavioral management in dentistry. They may feel anxious or uncomfortable or may not respond to any communication with the dentists. Patients with medical, physical, or psychological disorders may not cooperate and therefore require sedation (SED) or general anesthesia (GA) to receive dental treatment. Using the healthcare big data in Korea, this study aimed to analyze the trends of SED and GA in special needs patients undergoing dental treatment. It is believed that these data can be used as reference material for hospitals and for preparation of guidelines and related policy decisions of associations or governments for special needs patients in dentistry. Methods: The study used selected health information data provided by the Korean National Health Insurance Service. Patients with a record of use of one of the eight selected drugs used in dental SED between January 2007 and September 2019, those with International Classification of Diseases-10 codes for attention deficit hyperactivity disorder (ADHD), phobia, brain disease, cerebral palsy, epilepsy, genetic disease, autism, mental disorder, mental retardation, and dementia were selected. The insurance claims data were analyzed for age, sex, sedative use, GA, year, and institution. Results: The number of special needs patients who received dental treatment under SED or GA from January 2007 to September 2019 was 116,623. Number of SED cases was 136,018, performed on 69,265 patients, and the number of GA cases was 56,308, implemented on 47,257 patients. In 2007, 3100 special needs patients received dental treatment under SED while in 2018 the number of cases increased 6 times to 18,528 SED cases. In dentistry, ADHD was the most common disability for SED cases while phobia was the most common cause of disability for GA. The male-to-female ratio with respect to SED cases was higher for males (M : F = 64.36% : 35.64%). Conclusion The application of the SED method and GA for patients with special needs in dentistry is increasing rapidly; thus, preparing guidelines and reinforcing the education and system are necessary.

Chronotype is an important moderator of psychiatric illnesses, which seems to be controlled in some part by genetic factors. Clock genes are the most relevant genes for chronotype. In addition to the roles of individual genes, gene-gene interactions of clock genes substantially contribute to chronotype. We investigated genetic associations and gene-gene interactions of the clock genes BHLHB2, CLOCK, CSNK1E, NR1D1, PER1, PER2, PER3, and TIMELESS for chronotype in 1293 healthy Korean individuals. Regression analysis was conducted to find associations between single nucleotide polymorphism (SNP) and chronotype. For gene-gene interaction analyses, the quantitative multifactor dimensionality reduction (QMDR) method, a nonparametric model-free method for quantitative phenotypes, were performed. No individual SNP or haplotype showed a significant association with chronotype by both regression analysis and single-locus model of QMDR. QMDR analysis identified NR1D1 rs2314339 and TIMELESS rs4630333 as the best SNP pairs among two-locus interaction models associated with chronotype (cross-validation consistency [CVC] = 8/10, p = 0.041). For the three-locus interaction model, the SNP combination of NR1D1 rs2314339, TIMELESS rs4630333, and PER3 rs228669 showed the best results (CVC = 4/10, p < 0.001). However, because the mean differences between genotype combinations were minor, the clinical roles of clock gene interactions are unlikely to be critical.

Chronotype is an important moderator of psychiatric illnesses, which seems to be controlled in some part by genetic factors. Clock genes are the most relevant genes for chronotype. In addition to the roles of individual genes, gene-gene interactions of clock genes substantially contribute to chronotype. We investigated genetic associations and gene-gene interactions of the clock genes BHLHB2, CLOCK, CSNK1E, NR1D1, PER1, PER2, PER3, and TIMELESS for chronotype in 1293 healthy Korean individuals. Regression analysis was conducted to find associations between single nucleotide polymorphism (SNP) and chronotype. For gene-gene interaction analyses, the quantitative multifactor dimensionality reduction (QMDR) method, a nonparametric model-free method for quantitative phenotypes, were performed. No individual SNP or haplotype showed a significant association with chronotype by both regression analysis and single-locus model of QMDR. QMDR analysis identified NR1D1 rs2314339 and TIMELESS rs4630333 as the best SNP pairs among two-locus interaction models associated with chronotype (cross-validation consistency [CVC] = 8/10, p = 0.041). For the three-locus interaction model, the SNP combination of NR1D1 rs2314339, TIMELESS rs4630333, and PER3 rs228669 showed the best results (CVC = 4/10, p < 0.001). However, because the mean differences between genotype combinations were minor, the clinical roles of clock gene interactions are unlikely to be critical.

A ruptured brain arteriovenous malformation (bAVM) presenting with a hematoma may have unseen parts of the shunts in diagnostic angiography in the acute phase, which may lead to innate incomplete evaluation for the whole angioarchitecture of the bAVM. Even though it is generally accepted that the nidus of a ruptured bAVM may be underestimated in angiography during the acute phase due to hematoma compression, documentation of the underestimated parts has not been described in the literature. The authors report 2 cases of ruptured bAVMs in which the obscured segments were cast with liquid embolic material, which suggests a potential presence of obscured segments in bAVMs.