Non‐accidental burns (NABs) in children had some adverse effects, such as severe burns, requiring skin grafting, and mortality. Previous studies reported NABs in the form of neglect, suspected abuse, and child abuse. Also, different statistics were estimated for the prevalence of NABs in children. Therefore, the current study aimed to comprehensively review and summarise the literature on the prevalence of NABs in children. Also, factors related to NABs as a secondary aim were considered in this review. Keywords combined using Boolean operators and searches were performed in international electronic databases, such as Scopus, PubMed, and Web of Science. Only studies in English were considered from the earliest to 1 March 2023. The analysis was performed using STATA software version 14. Finally, 29 articles were retrieved for the quantitative analysis. Results found that the prevalence of child abuse, suspected abuse, neglect, ‘child abuse or suspect abused’, and ‘abuse, suspect abused, or neglect’ was 6% (ES: 0.06, 95% confidence interval [CI]: 0.05‐0.07), 12% (ES: 0.12, 95% CI: 0.09‐0.15), 21% (ES: 0.21, 95% CI: 0.07‐0.35), 8% (ES: 0.08, 95% CI: 0.07‐0.09), and 15% (ES: 0.15, 95% CI: 0.13‐0.16) among burns victims, respectively. Also, factors related to NABs are categorised into age and gender, agent and area of burns, and family features. Considering the results of the current study, planning for rapid diagnosis and designing a process to manage NABs in children is necessary.

The goal of this systematic review and meta‐analysis is to provide an overview of the prevalence of surgical wound infection and related factors in patients after long bone surgery. A comprehensive, systematic search was conducted in different international electronic databases, such as Scopus, PubMed, Web of Science and Persian electronic databases such as Iranmedex and Scientific Information Database using keywords extracted from Medical Subject Headings such as “Prevalence”, “Surgical wound infection”, “Surgical site infection” and “Orthopedics” from the earliest to the May 1, 2023. The appraisal tool for cross‐sectional studies (AXIS tool) evaluates the quality of the included studies. A total of 71 854 patients undergoing long bone surgery participated in 12 studies. The pooled prevalence of surgical wound infection in patients who underwent long bone surgery reported in the 12 studies was 3.3% (95% CI: 1.5%–7.2%; I2 = 99.39%; p < 0.001). The pooled prevalence of surgical wound infection in male and female patients who underwent long bone surgery was 4.6% (95% CI: 1.7%–11.7%; p < 0.001; I2 = 99.34%) and 2.6% (95% CI: 1.0%–6.3%; I2 = 98.84%; p < 0.001), respectively. The pooled prevalence of surgical wound infection in patients with femur surgery sites reported in nine studies was 3.7% (95% CI: 2.1–6.4%; I2 = 93.43%; p < 0.001). The pooled prevalence of surgical wound infection in open and close fractures was 16.4% (95% CI: 8.2%–30.2%; I2 = 95.83%; p < 0.001) and 2.9% (95% CI: 1.5%–5.5%; I2 = 96.40%; p < 0.001), respectively. The pooled prevalence of surgical wound infection in patients with diabetes mellitus (DM), hypertension (HTN) and cardiovascular disease (CVD) was 4.6% (95% CI: 2.3%–8.9%; I2 = 81.50%; p < 0.001), 2.7% (95% CI: 1.2%–6.0%; I2 = 83.82%; p < 0.001) and 3.0% (95% CI: 1.4%–6.4%; I2 = 69.12%; p = 0.006), respectively. In general, the different prevalence of surgical wound infection in patients undergoing surgical treatment after long bone fracture may be caused by underlying factors (gender and co‐morbidity) and fracture‐related factors (surgery site and type of fracture).

No abstract

No abstract

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital‐acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation–maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.