Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.

Objective:To explore the effect of proxy online health information seeking based on theory of planned behavior(TPB) of rural maternal for breast feeding guidance. The aim is to provide a theoretical basis for improving the rate of exclusive breast feeding among rural mothers.Methods:Using a quasi experimental research method and convenience sampling method, the study selected postpartum women and their primary caregivers who gave birth at Maoming Dianbai Maternal and Child Health Care hospital from October 2023 to April 2024 as the research subjects. According to the admission time, 47 parturients and their primary caregiver who gave birth between October and December 2023 were designated as the control group, while 50 parturients and their primary caregiver who gave birth between January and April 2024 were designated as the observation group. The control group received routine breast feeding education, while the observation group received proxy online health information seeking based on TPB breast feeding guidance. Compared the breast feeding knowledge, self-efficacy, family support, as well as the electronic health literacy and breast feeding knowledge of the primary caregivers of two groups of postpartum women.Results:The final control group included 40 maternal and their primary caregivers, while the observation group included 47 each. The maternal age of the control group was (36.73 ± 4.36) years, while that of the observation group was (35.96 ± 3.24) years. The main caregivers for the control group of parturients were 5 males and 35 females, aged 61.00 (59.00, 62.00) years old. There were 8 males and 39 females in the observation group, aged 62.00 (59.00, 64.00) years. After 6 months of intervention, the scores of breast feeding knowledge, self-efficacy, and family support were 14.00(12.00, 16.00), 45.67 ± 9.49, 3.00(3.00, 4.00) in the intervention group, more than those in the control group 7.00(2.00, 10.75), 37.15 ± 8.12, 2.00(2.00, 3.00) points, the differences were statistically significant between the two groups ( Z=-6.55, -5.68, t=-4.45, all P<0.05); the primary caregivers' electronic health literacy and breast feeding knowledge scores were 31.45 ± 4.92 and 14.00 (11.00, 16.00), respectively, higher than those of the control group family members 28.68 ± 4.46 and 4.50 (2.00, 9.75). The differences were statistically significant ( t=-3.38, Z=-7.19, both P<0.05). The pure breast feeding rate at 1 week, 3 months and 6 months postpartum were 70.2%(33/47), 63.8%(30/47), 59.6%(28/47) in the intervention group, more than those in the control group 47.5%(19/40), 37.5%(15/40), 27.5%(11/40), the differences were statistically significant between the two groups ( χ2=4.64, 6.00, 8.99, all P<0.05). Conclusions:Proxy online health information seeking based on TPB can effectively strengthen the health information searching ability of primary caregivers of rural maternal, and help rural maternal improve breastfeeding knowledge, self-efficacy and family support to promote exclusive breast feeding.

Objective:To explore the effect of proxy online health information seeking based on theory of planned behavior(TPB) of rural maternal for breast feeding guidance. The aim is to provide a theoretical basis for improving the rate of exclusive breast feeding among rural mothers.Methods:Using a quasi experimental research method and convenience sampling method, the study selected postpartum women and their primary caregivers who gave birth at Maoming Dianbai Maternal and Child Health Care hospital from October 2023 to April 2024 as the research subjects. According to the admission time, 47 parturients and their primary caregiver who gave birth between October and December 2023 were designated as the control group, while 50 parturients and their primary caregiver who gave birth between January and April 2024 were designated as the observation group. The control group received routine breast feeding education, while the observation group received proxy online health information seeking based on TPB breast feeding guidance. Compared the breast feeding knowledge, self-efficacy, family support, as well as the electronic health literacy and breast feeding knowledge of the primary caregivers of two groups of postpartum women.Results:The final control group included 40 maternal and their primary caregivers, while the observation group included 47 each. The maternal age of the control group was (36.73 ± 4.36) years, while that of the observation group was (35.96 ± 3.24) years. The main caregivers for the control group of parturients were 5 males and 35 females, aged 61.00 (59.00, 62.00) years old. There were 8 males and 39 females in the observation group, aged 62.00 (59.00, 64.00) years. After 6 months of intervention, the scores of breast feeding knowledge, self-efficacy, and family support were 14.00(12.00, 16.00), 45.67 ± 9.49, 3.00(3.00, 4.00) in the intervention group, more than those in the control group 7.00(2.00, 10.75), 37.15 ± 8.12, 2.00(2.00, 3.00) points, the differences were statistically significant between the two groups ( Z=-6.55, -5.68, t=-4.45, all P<0.05); the primary caregivers' electronic health literacy and breast feeding knowledge scores were 31.45 ± 4.92 and 14.00 (11.00, 16.00), respectively, higher than those of the control group family members 28.68 ± 4.46 and 4.50 (2.00, 9.75). The differences were statistically significant ( t=-3.38, Z=-7.19, both P<0.05). The pure breast feeding rate at 1 week, 3 months and 6 months postpartum were 70.2%(33/47), 63.8%(30/47), 59.6%(28/47) in the intervention group, more than those in the control group 47.5%(19/40), 37.5%(15/40), 27.5%(11/40), the differences were statistically significant between the two groups ( χ2=4.64, 6.00, 8.99, all P<0.05). Conclusions:Proxy online health information seeking based on TPB can effectively strengthen the health information searching ability of primary caregivers of rural maternal, and help rural maternal improve breastfeeding knowledge, self-efficacy and family support to promote exclusive breast feeding.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.