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OBJECTIVE We developed a colocation “Rapid Developmental Evaluation” (RDE) model for Developmental-Behavioral Pediatrics (DBP) to evaluate young children for developmental concerns raised during routine developmental surveillance and screening in a pediatric primary care Federally Qualified Health Center (FQHC). In this low-income patient population, we anticipated that colocation would improve patient access to DBP and decrease time from referral to first developmental evaluation and therapeutic services. METHODS Children were assessed at the FQHC by a DBP pediatrician, who made recommendations for therapeutic services and further diagnostic evaluations. A retrospective chart review over 27 months (N = 151) investigated dates of referral and visit, primary concern, diagnosis, and referral to tertiary DBP center and associated tertiary DBP center dates of service and diagnoses if appropriate. We surveyed primary care clinicians (PCCs) for satisfaction. RESULTS The DBP pediatrician recommended that 51% of children be referred to the tertiary DBP center for further diagnostic evaluation or routine DBP follow-up. Average wait from referral to an RDE visit was 57 days compared with 137.3 days for the tertiary DBP center. Children referred from RDE to the tertiary DBP center completed visits at a higher rate (77%) than those referred from other sites (54%). RDE-recommended therapeutic services were initiated for 73% of children by the tertiary visit. Fidelity of diagnosis between RDE and the tertiary DBP center was high, as was PCC satisfaction. CONCLUSION Colocation of a DBP pediatrician in an FQHC primary care pediatrics program decreased time to first developmental assessment and referral for early intervention services for an at-risk, low-income patient population.Research is critical to the growth of professional nursing in every practice area. Faith community nursing research evolved slowly in the years following publication of the first research in 1989. A faith community nursing research agenda was developed in 2008 and research priorities have been reviewed every 2 years since 2012 at a forum held in conjunction with the annual Westberg Symposium. This article reviews the progression and ongoing development of a research agenda for the specialty practice of faith community nursing. Recommendations for the development of future research for faith community nursing is discussed.OBJECTIVE The aim of the study was to assess safety and efficacy of 50-mg tramadol in reducing patient-perceived pain during colposcopy. MATERIAL AND METHODS We conducted a randomized double-blind placebo-controlled trial in the colposcopy unit of a tertiary referral hospital, Cairo, Egypt, from April 2018 to October 2018. Our primary outcome was pain during colposcopy-guided ectocervical punch biopsy. Our secondary outcomes were pain during speculum insertion, acetic acid application, Lugol iodine application, endocervical curettage (ECC), endocervical brushing, 10-minute postprocedure, and additional analgesia requirement. Pain was assessed using 10-cm visual analog scale. RESULTS One hundred fifty women were randomized into 2 groups tramadol group (n = 75) received oral 50-mg tramadol tablets, and control group (n = 75) received placebo tablets. Both groups showed no significant difference in anticipated pain score (p = .56), pain scores during speculum insertion (p = .70), application of acetic acid (p = .40), and Lugol iodine (p = .79). However, the mean pain scores were significantly lower in tramadol group compared with placebo at ectocervical biopsy (p = .001), ECC (p = .001), endocervical brushing (p = .001), and 10 minutes after colposcopy (p = .001). Need for additional analgesia was significantly lower in tramadol group (p = .03). CONCLUSIONS Oral tramadol 50 mg significantly reduces pain perception during colposcopy-guided ectocervical biopsy, ECC, endocervical brushing, and 10 minutes after colposcopy with tolerable adverse effects.OBJECTIVE The Accreditation Council for Graduate Medical Education and the Council on Resident Education in Obstetrics and Gynecology have milestones and/or competencies relating to colposcopy; however, the optimal way to reach these objectives is not proscribed and left to individual programs. Here, we aim to assess resident skill, confidence levels, perceived level of knowledge, and satisfaction with colposcopic training before and after implementation of a new interactive learning module with visual feedback. MATERIALS AND METHODS A new online educational intervention was developed by the author (E.L.N.) based on adult learning theory and introduced into our obstetrics and gynecology resident colposcopy curriculum in July 2014. We assessed performance on an objective competency examination administered at baseline and repeated after 6 months of our 24 residents.In addition, we assessed resident confidence levels, perceived level of knowledge, and satisfaction with training before and 6 months after intervention. APX2009 mw RESULTS Scores on a national online examination improved after the intervention (p = .014). Significant improvements on the examination were seen in the sections of medical knowledge (p = .031) and management (p = .011). Residents’ perceived knowledge increased significantly after the intervention (p = .030). CONCLUSIONS Learning outcomes improved after introduction of a novel teaching intervention.OBJECTIVES To develop an accurate machine learning (ML) predictive model incorporating patient, fracture, and trauma characteristics to identify individual patients at risk of an (occult) PMF. METHODS Databases of 2 studies including patients with TSFs from 2 Level 1 trauma centers were combined for analysis. Using ten-fold cross-validation, 4 supervised ML algorithms were trained in recognizing patterns associated with PMFs (1) Bayes point machine; (2) support vector machine; (3) neural network; and (4) boosted decision tree. Performance of each ML algorithm was evaluated and compared based on (1) C-statistic; (2) calibration slope and intercept; and (3) Brier score. The best-performing ML algorithm was incorporated into an online open-access prediction tool. RESULTS Total data set included 263 patients, of which 28% had a PMF. Training of the Bayes point machine resulted in the best-performing prediction model reflected by good C-statistic, calibration slope, calibration intercept, and Brier score of 0.89, 1.