PURPOSE: To determine the diagnostic value of eosinopenia and the neutrophil-to-lymphocyte ratio (NLR) in the diagnosis of early onset neonatal sepsis (EONS). METHODS: This cross-sectional study was conducted in the Neonatology Ward of R.D. Kandou General Hospital Manado between July and October 2017. Samples were obtained from all neonates meeting the inclusion criteria for EONS. Data were encoded using logistic regression analysis, the point-biserial correlation coefficient, chi-square test, and receiver operating characteristic curve analysis, with a P value <0.05 considered significant. RESULTS: Of 120 neonates who met the inclusion criteria, 73 (60.8%) were males and 47 (39.2%) were females. Ninety (75%) were included in the sepsis group and 30 (25%) in the nonsepsis group. The mean eosinophil count in EONS and non-EONS groups was 169.8±197.1 cells/mm³ and 405.7±288.9 cells/mm³, respectively, with statistically significant difference (P<0.001). The diagnostic value of eosinopenia in the EONS group (cutoff point: 140 cells/mm³) showed 60.0% sensitivity and 90.0% specificity. The mean NLR in EONS and non-EONS groups was 2.82±2.29 and 0.82±0.32, respectively, with statistically significant difference (P<0.001). The diagnostic value of NLR in the EONS group (cutoff point, 1.24) showed 83.3% sensitivity and 93.3% specificity. CONCLUSION: Eosinopenia has high specificity as a diagnostic marker for EONS and an increased NLR has high sensitivity and specificity as a diagnostic marker for EONS.
Cross-Sectional Studies ; Diagnosis ; Eosinophils ; Female ; Hospitals, General ; Humans ; Infant, Newborn ; Logistic Models ; Lymphocytes ; Male ; Neonatology ; Neutrophils ; ROC Curve ; Sensitivity and Specificity ; Sepsis

Objectives: The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum. Methods: An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree. Results: A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools. Conclusions The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.

Objectives: The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum. Methods: An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree. Results: A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools. Conclusions The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.

Objectives: The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum. Methods: An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree. Results: A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools. Conclusions The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.

Objectives: The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum. Methods: An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree. Results: A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools. Conclusions The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.