RESUMO
Background Drug-drug interactions (DDIs) can have serious consequences for patient health and well-being. Patients who are taking multiple medications may be at an increased risk of experiencing adverse events or drug toxicity if they are not aware of potential interactions between their medications. Many times, patients self-prescribe medications without knowing DDI. Objective The objective is to investigate the effectiveness of ChatGPT, a large language model, in predicting and explaining common DDIs. Methods A total of 40 DDIs lists were prepared from previously published literature. This list was used to converse with ChatGPT with a two-stage question. The first question was asked as "can I take X and Y together?" with two drug names. After storing the output, the next question was asked. The second question was asked as "why should I not take X and Y together?" The output was stored for further analysis. The responses were checked by two pharmacologists and the consensus output was categorized as "correct" and "incorrect." The "correct" ones were further classified as "conclusive" and "inconclusive." The text was checked for reading ease scores and grades of education required to understand the text. Data were tested by descriptive and inferential statistics. Results Among the 40 DDI pairs, one answer was incorrect in the first question. Among correct answers, 19 were conclusive and 20 were inconclusive. For the second question, one answer was wrong. Among correct answers, 17 were conclusive and 22 were inconclusive. The mean Flesch reading ease score was 27.64±10.85 in answers to the first question and 29.35±10.16 in answers to the second question, p = 0.47. The mean Flesh-Kincaid grade level was 15.06±2.79 in answers to the first question and 14.85±1.97 in answers to the second question, p = 0.69. When we compared the reading levels with hypothetical 6th grade, the grades were significantly higher than expected (t = 20.57, p < 0.0001 for first answers and t = 28.43, p < 0.0001 for second answers). Conclusion ChatGPT is a partially effective tool for predicting and explaining DDIs. Patients, who may not have immediate access to the healthcare facility for getting information about DDIs, may take help from ChatGPT. However, on several occasions, it may provide incomplete guidance. Further improvement is required for potential usage by patients for getting ideas about DDI.
RESUMO
Background and objective Patients suffering from thalassemia major are at higher risk of osteoporosis. Due to their decreased life expectancy, the number of adult patients is low. However, their bone health is rarely checked in developing countries like India. There is no data available in the literature on the bone mineral density (BMD) of adult (aged ≥18 years) thalassemia major patients in eastern India. In this study, we aimed to measure the BMD and serum calcium and vitamin D levels in adult thalassemia major patients and to compare them with healthy controls. Materials and methods We conducted this cross-sectional observational study at a tertiary care hospital in eastern India. We recruited adult thalassemia major patients who were not on calcium or vitamin D supplements. Their BMD was measured by dual-energy X-ray absorptiometry (DXA) on the lumbar spine (L1-L4). Venous blood was tested for serum calcium and vitamin D levels. We compared the parameters between the cases and controls by using the Mann-Whitney U test. Results A total of 31 (male = 19, female = 12) patients with a median age of 28 years comprised the case group. Age- and sex-matched controls showed similar height but higher weight and BMI. The serum calcium level was similar (p = 0.43) in the case and control groups but T-score (p = 0.0003) and vitamin D levels (p: <0.0001) were significantly lower in thalassemia major patients. Conclusion Based on our findings, adult thalassemia major patients have lower BMD and vitamin D levels. Although the serum calcium may be normal in these patients, they should still be screened both for BMD and vitamin D for prompt and early detection of risks and complications so that a proper management strategy can be implemented.
