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a b s t r a c t: Objective: To study the impact of target volume changes in brain metastases during fractionated stereotactic radiosurgery (fSRS) and identify patients that benefit from MRI guidance. Material and methods: For 15 patients (18 lesions) receiving fSRS only (fSRSonly) and 19 patients (20 lesions) receiving fSRS postoperatively (fSRSpostop), a treatment planning MRI (MR0) and repeated MRI during treatment (MR1) were acquired. The impact of target volume changes on the target coverage was analyzed by evaluating the planned dose distribution (based on MR0) on the planning target volume (PTV) during treatment as defined on MR1. The predictive value of target volume changes before treatment (using the diagnostic MRI (MRD)) was studied to identify patients that experienced the largest changes during treatment. Results: Target volume changes during fSRS did result in large declines of the PTV dose coverage up to 34.8% (median = 3.2%) for fSRSonly patients. For fSRSpostop the variation and declines were smaller (median PTV dose coverage change = 0.5% (4.5% to 1.9%)). Target volumes changes did also impact the minimum dose in the PTV (fSRSonly; 2.7 Gy (16.5 to 2.3 Gy), fSRSpostop; 0.4 Gy (4.2 to 2.5 Gy)). Changes in target volume before treatment (i.e. seen between the MRD and MR0) predicted which patients experienced the largest dose coverage declines during treatment. Conclusion: Target volume changes in brain metastases during fSRS can result in worsening of the target dose coverage. Patients benefiting the most from a repeated MRI during treatment could be identified before treatment.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
MULTIFILE
Background: The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. Objective: The primary objectives of this study are to evaluate whether a personalized mobile app can improve children’s on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone–based app on vaccination improvement. Methods: A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children’s 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers’ perceptions about RCI and a mobile phone–based app in improving RCI coverage. Results: Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. Conclusions: This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs.