WHAT'S KNOWN ON THIS SUBJECT: Although unnecessary for children with headache and normal history, computed tomography (CT) scans are widely used. Fewer than 1% of pediatric brain abnormalities present with headache as the only symptom. Furthermore, repeated CT scans may increase lifetime risk of cancer.WHAT THIS STUDY ADDS: CT scans continue to be used to diagnose isolated pediatric headaches despite existing practice parameters. Although emergency department visits were correlated with greater likelihood of CT scan use, these scans were widely used across a variety of clinical settings. abstract OBJECTIVE: Although unnecessary for children with headache and normal history, computed tomography (CT) scans are widely used. This study sought to determine current practice patterns of neuroimaging to diagnose pediatric headache in a variety of treatment settings and to identify factors associated with increased use of neuroimaging. METHODS:This retrospective claims analysis included children (aged 3-17 years) with $2 medical claims for headache. The primary outcome was CT scan utilization on or after first presentation with headache in a physician' s office or emergency department (ED). RESULTS:Of 15 836 patients, 26% (4034 patients; mean age: 11.8 years) had $1 CT scan, 74% within 1 month of index diagnosis. Patients with ED visits were 4 times more likely to undergo a CT scan versus those without ED visits (P , .001 [95% confidence interval: 3.9-4.8]). However, even outside the ED, use of CT scans remained widespread. Two-thirds of patients with CT scans had no ED use. Among patients with no ED utilization, .20% received a CT scan during the study period. Evaluation by a neurologist was strongly associated with a lower likelihood of CT scan compared with other provider specialties (odds ratio: 0.37; P , .01 [95% confidence interval: 0.30-0.46]).CONCLUSIONS: Use of CT scans to diagnose pediatric headache remains high despite existing guidelines, low diagnostic yield, and high potential risk. Implementing quality improvement initiatives to ensure that CT scans in children are performed only when truly indicated will reduce unnecessary exposure to ionizing radiation and associated cancer risks. Pediatrics 2013;132:e1-e8 AUTHORS:
The health plan-sponsored ERUMI program, consisting of both financial and educational components, decreased nonurgent ED utilization while increasing the use of alternative treatment sites.
Use of CT scans to diagnose pediatric headache remains high despite existing guidelines, low diagnostic yield, and high potential risk. Implementing quality improvement initiatives to ensure that CT scans in children are performed only when truly indicated will reduce unnecessary exposure to ionizing radiation and associated cancer risks.
This study compares the price predictions of the Vanguard real estate exchange-traded fund (ETF) (VNQ) using the back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models. The input variables for BPNN include the past 3-day closing prices, daily trading volume, MA5, MA20, the S&P 500 index, the United States (US) dollar index, volatility index, 5-year treasury yields, and 10-year treasury yields. In addition, variable reduction is based on multiple linear regression (MLR). Mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to measure the prediction error between the actual closing price and the models’ forecasted price. The training set covers the period between January 1, 2015 and March 31, 2020, and the forecasting set covers the period from April 1, 2020 to June 30, 2020. The empirical results reveal that the BPNN model’s predictive ability is superior to the ARIMA model’s. The predictive accuracy of BPNN with one hidden layer is better than with two hidden layers. Our findings provide crucial market factors as input variables for BPNN that might inspire investors in VNQ markets. JEL classification numbers: C32, C45, C53, G17. Keywords: Vanguard real estate ETF (VNQ), Back propagation neural network (BPNN), Autoregressive integrated moving average (ARIMA), Multiple linear regression (MLR).
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