Citations

• Artificial intelligence implementation in the management of patients with tuberculosis
  Erlina Wijayanti, Ummi Azizah Rachmawati, Citra Fitri Agustina
  Korean Journal of Family Medicine.2025; 46(1): 52.     CrossRef
• Integrating Machine Learning for Personalized Fracture Risk Assessment: A Multimodal Approach
  Sheikh Mohd Saleem, Shah Sumaya Jan
  Korean Journal of Family Medicine.2024; 45(6): 356.     CrossRef

Medication review is an intervention with the potential to reduce drug-related problems (DRPs) in the elderly. This study aimed to determine the effect of pharmacists’ medication reviews on geriatric patients. This study accessed two online databases, MEDLINE Complete and Scopus, and examined all studies published in English between 2019 and 2023, except for reviews. The studies included (1) participants over 65 years of age and (2) medication reviews conducted by pharmacists. The titles, abstracts, and full texts were reviewed for data extraction to determine whether the studies satisfied the inclusion and exclusion criteria. Forty-four of the initial 709 articles were included in this study. The articles included discussions on the incidence rates of DRPs and potentially inappropriate medications (PIMs) (n=21), hospitalization (n=14), medication adherence (n=9), quality of life (QoL) (n=8), and falls (n=7). Pharmacist medication reviews were associated with a reduced incidence of DRPs and PIMs, and improved adherence to medications. Patients’ overall QoL is also increasing. However, pharmacist medication reviews were not strongly associated with decreased hospitalization or falls. A pharmacist’s medication review may be a feasible intervention for reducing the incidence rates of DRPs and PIMs, regardless of whether it is performed as a sole intervention or supplemented with other interventions. The intervention was also effective in increasing medication adherence and QoL.

Citations

• Quality of prescribing and health-related quality of life in older adults: a narrative review with a special focus on patients with atrial fibrillation and multimorbidity
  Cheima Amrouch, Deirdre A. Lane, Amaia Calderón-Larrañaga, Mirko Petrovic, Delphine De Smedt
  European Geriatric Medicine.2025;[Epub]     CrossRef
• Erratum: The Impact of Pharmacist Medication Reviews on Geriatric Patients: A Scoping Review
  Nor Liana Che Yaacob, Mathumalar Loganathan, Nur Azwa Hisham, Habibah Kamaruzzaman, Khairil Anuar Md Isa, Mohamed Izham Mohamed Ibrahim, Kwok-Wen Ng
  Korean Journal of Family Medicine.2024; 45(4): 235.     CrossRef
• Application of the Statin-Associated Muscle Symptoms-Clinical Index to a Cohort of Patients with Type 2 Diabetes Mellitus Undergoing Phlebotomy at an Endocrinology Clinic
  Nor Humaira Mohd Tajudin, Mathumalar Loganathan Fahrni, Rohana Abdul Ghani, Mohd Hazriq Awang, Hitesh Chopra, Ali Saleh Alkhoshaiban
  Journal of Pharmacology and Pharmacotherapeutics.2024; 15(4): 389.     CrossRef
• Contribución de los Farmacéuticos Comunitarios en la detección y notificación de reacciones adversas en Andalucía
  Miguel Romero Pérez, Manuel Sánchez Polo, José Alberto Ayala Ortiz, Blanca Contreras Aguilar, María José Zarzuelo-Romero
  Ars Pharmaceutica (Internet).2024; 66(1): 25.     CrossRef

Shared decisions, in which physicians and patients share their agendas and make clinical decisions together, are optimal for patient-centered care. Shared decision-making (SDM) training in family medicine residency is always provided, but the best training approach for improving clinical practice is unclear. This review aims to identify the scope of the literature on SDM training in family medicine residency to better understand the opportunities for training in this area. Four databases (Embase, MEDLINE, Scopus, and Web of Science) were searched from their inception to November 2022. The search was limited to English language and text words for the following four components: (1) family medicine, (2) residency, (3) SDM, and (4) training. Of the 522 unique articles, six studies were included for data extraction and synthesis. Four studies referenced three training programs that included SDM and disease- or condition-specific issues. These programs showed positive effects on family medicine residents’ knowledge, skills, and willingness to engage in SDM. Two studies outlined the requirements for SDM training in postgraduate medical education at the national level, and detailed the educational needs of family medicine residents. Purposeful SDM training during family medicine residency improves residents’ knowledge, skills, and willingness to engage in SDM. Future studies should explore the effects of SDM training on clinical practice and patient care.

Background
Predicting the risk of osteoporotic fractures is vital for prevention. Traditional methods such as the Fracture Risk Assessment Tool (FRAX) model use clinical factors. This study examined the predictive power of the FRAX score and machine-learning algorithms trained on FRAX parameters.
Methods
We analyzed the data of 2,147 female participants from the Ansan cohort study. The FRAX parameters employed in this study included age, sex (female), height and weight, current smoking status, excessive alcohol consumption (>3 units/d of alcohol), and diagnosis of rheumatoid arthritis. Osteoporotic fracture was defined as one or more fractures of the hip, spine, or wrist during a 10-year observation period. Machine-learning algorithms, such as gradient boosting, random forest, decision tree, and logistic regression, were employed to predict osteoporotic fractures with a 70:30 training-to-test set ratio. We evaluated the area under the receiver operating characteristic curve (AUROC) scores to assess and compare the performance of these algorithms with the FRAX score.
Results
Of the 2,147 participants, 3.5% experienced osteoporotic fractures. Those with fractures were older, shorter in height, and had a higher prevalence of rheumatoid arthritis, as well as higher FRAX scores. The AUROC for the FRAX was 0.617. The machine-learning algorithms showed AUROC values of 0.662, 0.652, 0.648, and 0.637 for gradient boosting, logistic regression, decision tree, and random forest, respectively.
Conclusion
This study highlighted the immense potential of machine-learning algorithms to improve osteoporotic fracture risk prediction in women when complete FRAX parameter information is unavailable.

Citations

• Clinical Applicability of Machine Learning in Family Medicine
  Jungun Lee
  Korean Journal of Family Medicine.2024; 45(3): 123.     CrossRef
• Integrating Machine Learning for Personalized Fracture Risk Assessment: A Multimodal Approach
  Sheikh Mohd Saleem, Shah Sumaya Jan
  Korean Journal of Family Medicine.2024; 45(6): 356.     CrossRef

Background
In patients with breast cancer, a healthy diet can help reduce breast cancer-specific recurrence, mortality, and comorbid chronic disease rates. There have been few studies on dietary habits immediately after breast cancer diagnosis, especially those involving the Asian population. Therefore, this study aimed to compare the nutritional habits of newly diagnosed patients with breast cancer and the general population without cancer in Korea using propensity score (PS) matching.
Methods
We conducted a case-controlled study of 157 patients with breast cancer and 2,363 cancer-free control participants from the Korea National Health and Nutrition Examination Survey. The PS values for the predicted probability of patients with breast cancer and the general population were estimated using logistic regression analysis, including age and body mass index. The dietary patterns were assessed using a 24-hour recall of 1 day and the Food Frequency Questionnaire.
Results
PS matching showed that patients with breast cancer consumed fewer calories and carbohydrates; however, they consumed more protein and fat compared to the general population. Compared to the general population, patients with breast cancer consumed more healthy foods such as fish, seaweed, vegetables, fruit, mixed-grain rice, and nuts; however, they also consumed more soup, stew, and red meat.
Conclusion
Newly diagnosed patients with breast cancer have some healthy dietary habits compared to the general population. However, there is considerable room for improvement in their diet quality. Our results support the need to develop tailored dietary recommendations for patients with breast cancer during the diagnostic and posttreatment periods to improve their diet quality.

Background
Evidence on the association between obesity parameters, including body mass index (BMI) and waist circumference (WC), and osteoarthritis is limited. This study aimed to investigate these associations in Korean adults.
Methods
This nationwide cross-sectional study used data from 24,101 adults aged ≥19 years who participated in the Korea National Health and Nutrition Examination Survey 2016–2020. Odds ratios (ORs) and 95% confidence intervals (CIs) for osteoarthritis according to BMI and WC were analyzed using multivariable logistic regression analyses.
Results
The prevalence of osteoarthritis was higher in individuals with general (10.0%) and abdominal obesity (12.8%) compared with those without. Greater BMI and WC were associated with a higher prevalence (P<0.001) and risk of osteoarthritis (Model 3, P for trend <0.001). Individuals with general and abdominal obesity were associated with a 1.50-fold (OR, 1.50; 95% CI, 1.35–1.67) and 1.64-fold (OR, 1.64; 95% CI, 1.47–1.84) increased risk of osteoarthritis, compared with those without. Similar associations were observed in subgroups according to age, sex, smoking status, and presence of diabetes mellitus. The odds of osteoarthritis 1.73-fold increased (OR, 1.73; 95% CI, 1.53–1.95) in individuals with both general and abdominal obesity compared with those without any of them.
Conclusion
Greater BMI, WC, and general and abdominal obesity were associated with an increased risk of osteoarthritis in Korean adults. Appropriate management of abdominal and general obesity may be important to reduce the risk of osteoarthritis.

Citations

• Association between waist circumference or weight change after smoking cessation and incidence of cardiovascular disease or all-cause death in Korean adults with type 2 diabetes
  Heajung Lee, Jaeyong Shin, Jae Woo Choi
  Frontiers in Endocrinology.2024;[Epub]     CrossRef

Background
Physical activity (PA) is associated with a favorable metabolic risk profile in adults. However, its role in adolescents remains unclear. In this study, using data (2019–2021) from the 8th Korea National Health and Nutrition Examination Survey, we investigated the optimal exercise type for preventing metabolic complications in adolescents.
Methods
A total of 1,222 eligible adolescent participants (12–18-year-old) were divided into four groups as follows: aerobic exercise (AE), resistance exercise (RE), combined aerobic and resistance exercise (CE), and no exercise (NE). Daily PA was assessed using the international PA questionnaire. Blood samples were collected to measure lipid, glucose, and insulin levels. Additionally, the homeostasis model assessment for insulin resistance (HOMA-IR) and triglyceride-glucose (TyG) indices were measured. Multivariate regression analysis was used to compare the metabolic risk factors across the PA groups before and after propensity score matching (PSM) adjustment for confounding variables.
Results
The CE group exhibited improved fasting glucose levels, lower TyG index, reduced white blood cell count, and higher high-density lipoprotein (HDL) cholesterol levels than the NE group. The RE group exhibited lower mean blood pressure, triglyceride, fasting insulin, HOMA-IR, TyG index and a reduced risk of metabolic syndrome than the NE group. The AE group had higher total and HDL cholesterol levels. In detailed comparison of the AE and RE groups, the RE group consistently exhibited favorable metabolic parameters, including lower blood pressure and total and low-density cholesterol levels, which persisted after PSM.
Conclusion
These findings highlight the positive effects of PA on cardiovascular risk factors in adolescents. Thus, RE may have a more favorable metabolic effect than AE. Further studies are needed to validate the benefits of exercise according to the exercise type.

Citations

• Reflecting on progress and challenges: the Korean Journal of Family Medicine in 2024
  Seung-Won Oh
  Korean Journal of Family Medicine.2025; 46(2): 55.     CrossRef