A Randomized Controlled Trial to Evaluate the Effect of Pulsed Radiofrequency as a Treatment for Anterior Cutaneous Nerve Entrapment Syndrome in Comparison to Anterior Neurectomy

BACKGROUND: Chronic abdominal pain can be due to entrapped intercostal nerves (anterior cutaneous nerve entrapment syndrome [ACNES]). If abdominal wall infiltration using an anesthetic agent is unsuccessful, a neurectomy may be considered. Pulsed radiofrequency (PRF) applies an electric field around the tip of the cannula near the affected nerve to induce pain relief. Only limited retrospective evidence suggests that PRF is effective in ACNES. METHODS: A multicenter, randomized, nonblinded, controlled proof-of-concept trial was performed in 66 patients. All patients were scheduled for a neurectomy procedure. Thirty-three patients were randomized to first receive a 6-minute cycle of PRF treatment, while the other 33 were allocated to an immediate neurectomy procedure. Pain was recorded using a numeric rating scale (NRS, 0 [no pain] to 10 [worst pain possible]). Successful treatment was defined as >50% pain reduction. Patients in the PRF group were allowed to cross over to a neurectomy after 8 weeks. RESULTS: The neurectomy group showed greater pain reduction at 8-week follow-up (mean change from baseline -2.8 (95% confidence interval [CI] -3.9 to -1.7) vs. -1.5 (95% CI -2.3 to -0.6); P = 0.045) than the PRF group. Treatment success was reached in 12 of 32 (38%, 95% CI 23 to 55) of the PRF group and 17 of 28 (61%, 95% CI 42 to 72) of the neurectomy group (P = 0.073). Thirteen patients were withdrawn from their scheduled surgery. Adverse events were comparable between treatments. CONCLUSIONS: PRF appears to be an effective and minimally invasive treatment option and may therefore be considered in patients who failed conservative treatment options before proceeding to a neurectomy procedure. Anterior neurectomy may possibly lead to a greater pain relief compared with PRF in patients with ACNES, but potential complications associated with surgery should be discussed

Monthly evaporation forecasting using artificial neural networks and support vector machines

Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and epsilon-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, epsilon-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R (2)). According to the performance criteria, the ANN algorithms and epsilon-SVR had similar results. The ANNs and epsilon-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R (2) = 0.905