This clinical trial is a prospective, parallel, randomized, double-blinded, controlled study that included 70 obese patients with BMI > 40kg/m2 and with BMI > 35kg/m2 with underlying diseases such as diabetes, hypertension, renal disease, and cardiovascular diseases, and American Society of Anesthesiologists (ASA) physical status 1 & 2, who are candidates for elective laparoscopic Reux-en Y bypass under general anesthesia and orotracheal intubation, at Iranmehr hospital, within 3 months from December 2017 until March 2018.
This study is conducted on a pilot group and the sample size was determined based on the variable "Number of patients who would need the treatment".
According to the pilot study results, the relative frequency of the need for re-treatment in group K was 25%, and in group M was 52%. By taking these results and the sample size formula (Formula.1), the required sample size was at least 36 patients in each group.
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K7j28WrCTPLZprhvcgzrc+iyqayT99lBOZRsLbt6Vp+CW6rP4urAYK05LJytUSnRBP0O+xhJ3y9EMs/+Z7Nvkm0GNYH5pflAmhJu4ExbYmvp0LB4/u5HjWpYNNJ09/oqPD8Ug9wYH96RjqcTmifv7pb30hBKAqszm+4xC5xS70jwqqXXYMayF7Vn7JjKWbOdmkH89PeI7b2jWu0fuXl8JmNqIvr+SPV50GDTZKi8sx22xYKkrJLy7Cza7OqyxpcfH0wsvNufaV5ooUVs7+iJm/ZtHhqalMGtPjb5DBREdwXAdaddMxb/82TuYNJihEi0bnQ/cH55A/ejYaZb8gt75G64q7x6XTplbJks/RxDyKIvsSGVxD5cyaR9L+PLxChxDrVUJ+rkJV5nL40AbmvjSNde4jmf7dWwwLqUeYZtdzYvc2dmj8eKp56GUd7qdsJsr1FVista+KK6VB5+KOl5d7zSlGy3I4llqKU4+mBFFKfq4Ja0UxKbt+5KOZc9jsfT3/ef8pBib41T2w9Yynb9/mfP7eDnYdLaBXlPeZbGWXLLQVY0UFBpP1vDUINGg1hZRWmLBZDZSVFJFf5Iqrsv/xW3NyxdPTA1fnGgJUSwWZmfnovJvQMsiFsrw8TJoyUg7u45evZvL1bldue/51Hh/eCu/6RPCmIpKOHuWQTceg2FD8GzzV7B+U1UyFoRKTuQ4zPJQGnYsbHl7uONcwbk0Z9eRl5ZAfEEj/GDdMRfmUV5SRdXgLy5d9zZw9Gu585W1euKUVdc/npz0zPMrVRGZqKqkFeoi4xBw8nT8x7dvgF7adzQeP8OANrWki42uvCAkmhBBYSwtIyS2hyLc5/kFe1Nz2ZiZtxwpmfPo1O7SdGHb/44weGHNl1zmoDWWjsjSfnHItIWFBeDnXo0ZuryT5pw8YeeMEjla1zxvDiXzjz3/0Z+CkRSx7fsjFk3IvSc+BHz/n3U9XUdrxEV54ZDixHlcikrBjKM7ixOlsTFYNHoExNI0PrroCCGi1zri5WSg4mUJaUQU9QryBs5XZ+jTwnq80n9P6EnDS1tgybU/ezYptGRR7z+fRwUtAgbIYce9+G0++u4ypQ9sS5FzP/h5zKakpWSi/SOJCLm+3hPHw1wy/42l+O37RpJRquNFm0AvM/OoVeodVX80rO7GXLQVlFB39L4/e/DVOWrCaXYjucDW3vbiQ//RqQ6iXo9UIJ1zd3XHSZpCYVYDZGou7y6WfILbKo3zx0H08t3Af+irONu6azxn35z/G3smn8yfzQN+YS695cpa5PI0TOw6QeyyTT57Zx1wXLSg7+F7Fv8Y+wddvD6BNuDf1nS5hN5SSlVOA1c2P8CA/XC/bA1NReGgZzzzzAvM3pNbhOE/aXvMUH0x/niHNvKt5nisqirM4eOIQh08nU/bMKBY7g93mhE9Ie254+FnWvN6DtlF17pI4S4Orhw8hfgGonSXk5JcDlwgmNBqcnXVoKCM5NZ38SgtNPP5pqcgbBwkmhBCY9aXkVJZTpqEWAYGi7NRWvvx4DqsSg/jXS+N5/pFBhDT6lnKwG9L5ZfpEpqW0Y/LER+hx0eTHunDGK6IVg6+9jhjthUMsrGWZ7N+diDW2I1cl+KM911SqFBrnQLq2CqWKetOfWMnZ+QNTJv6XvU6D+fDNJ7mumU/NhzU4E2m/fsOcb5azan8ervZKSisTuO3Zp3n8zqsIqOK713n4EhoQDNlF5JZUApejcq1BU8swNuf4fo46BzPyw3V8c3f8ZSgL2C3lFGflAOGXPfOOk18sA4cMxzO2CF2tJrnasZg8aNa9Tc3rQmAh49hRigojGfvxF7x7R+taNDLUhTuB0SG4euhJySvGYrNDFf0bGp0vCd2vZoQhkmKz+n2yt0ZjJu/EYU7kuRLXrikRvm5ozvVL2K3YY7sSH1zTAoCKyuxT7E+20frW95j54b00b5hFRi5iMeopKyrCZg247A0vrn5RdOt3LfnuaWhrNdfCjs3iS8uebQnzrikLnYXy/DTSk2z0v+s9pn/yAK0u1/QtDdW8lHR4+QYS6OpBZWYRhQYzONAPIupPggkhBGjOjDmuzSvHWprEmvmz+Wp9Lh1uf5qxd/SkaU1Df5WNktQD7M02E9GiMy0DGrZaUjs2ipKP8NsBM10Gd6FV4JmJr1ZjIacO7WHrz1tJsbriFdeZIUMG0D7UvfrPQ+tCWLfbmfbj7RdtsiZ9x53DXqB03H/48bFODpfYmLWHBdOmsuykP6Pff5JR/aJqHk5iLOD4vq0c9erOTe1CG6DSYqdg71Jef+1jcnqMY/HPo0lwzWfhv29h3MT/0rl/Z25oUvWrRCl15v66XNUnpf5YxbpaZRzeuBWDtgPXda3l9GpzKcfWb6MotDVdOkbjVqtL0ID2r4msXWIG8tZHAy/T2fM59NtBsjRN6dCsdpVfZSni4I6j5Ds1oWePGDyrPUih7Iozw5WqP7vWNYbBT7zP4Cf+vMXA5kljeer7EF6ZNYGbWjkSaFvISTzEXrsH7bslEFjNl6xsVkyFyWzbvIVdR9MxOfnQvOdABvVoQ7B77Xq0zs3xuLw0eMf15rEJvXnscpzeVklB0lEOJ0H0TS2Jqi6QsJspLUhh/8ZN7DqWQaVHENFdBzG8d2uCq21R0aBFi7Lbz666fulP7ex67KC9jM8YUaO/QVuiEOKyUwqlVM2TIM2F7Fk+l2mLfsPU/SYeHXcbXWqYRGjJPciXHz7Lzbffxfip89mZbW6wYteJpZikPds44hpN955t8XWyU5y4kXmPP8j4Vxeyr8gK5SdZMfUl/j3mPTaecjQ7iJ2KikosdjtWUyUmR89iSGPtzMm8vTKNlvc9x7Oj+xNcbX2lkuTtixg35l7uGv0gk9elNMyr1ZjCii8WsE43gOceu5MEdy3YXAmMD8Wi8ikqq2Z9Ac2ZldGxK+yXq5ne3ZMQV7ffA+IqGU6yeWMquCcQG1ZDj5Qyk7tnKRMeG8WIWx5n5prjVNay+AqFsttr3rGxyz3NztQMyptGER7gVkPLo5XM7cuYfM8obrnnGaZ9c5iyGv8DDZqzQZfNXotnzyXpqTSbsdlMVFRU4NCTxVpE0v7jWGx+NAsLxL3KsUyVnFg/l38//iJTl+8h32Si8OCPTHzuKZ76ZA2nq/sd/E6hlB0aYd6uurBVlHDy1DGOe/sSHB9cdX+jrYwTq+fyxpin+WjZAYrMRnIPrWLK4+OZ8P6PpFczY9pqNlBqKAcfD3y93Kp8lp0LIZTdjqN3kag/CSaEEDh7eBPk5oUnmrOtyJegjKTuWsGnM5aT7NKTh8fdzzWtAn9/iChzEUeWL+eHhdvItwN2Pae3f8uLH/9CgfLGy8NOZrEN9/rMUziPOf8YSz5+jquvHsqoRyexfH8GmUdW88azT/Di/N8o/FMt3lyUydFduYRHt6ZzrB9gpjTjGLvXHMDr6gd484PJvPn+VCbf3YaMzYtZuDWF2i3D1fCUtYwjq2bx2se/4tllLG89dzstPH/fijH/EEtfmcO2vLMl1J9k3oxZzN5YTNMYd07mmnDTNUyup/KUA2zcVkCbLn1pE3GmEm6vOM3OXxIJatKTDvFVB5N2o57C0iII9SHI5zJlrvEKpVVkEJ65uRSWV1OdzDjGhvRSVKvmRFWzpoUy5bFp3mt8sLWUoEAfsOgxObnWery8k84T35AgatPi3piVZpwkKaeMkKbRhHtWXZmDLFa/8jZLtmfjHh6ISslH6+pUiyymJkpzCjEb3YkK9Kn3onOOshuySdx5EidzEDEhflT5s7FXcHrnfvalBTHikZeYPHEi0z59lRvcM/h1zo/sTSmlpgS3OhdPvHz90ep0jSzNa10ojPp8kpOSsQWEkhB56dTHAMqmJ+PQXk7kejL0ydeYNOldPpz4ErfF5LBy2Q9sS6us4kgrFWV55OUU4x8RSZPgqnqcbFRWlFJiNuAW6oefqwxxulJkmJMQAp1vELHBPgQcLqK06EwL34UDkRTlydtY8MFs1iW6M/zVMdw9oAXev7//LeQe+ZU581dg7jma/lpAuRPeqi+PRvTF35aC+fRmNic1RGmtFBz+if9MnMb2gIE8MP7fGPduYvXHU9jnlcOmwxpuHdkEnwsany0UpiexM8+ZiB5tCfcEcCWyx51M2DQcbVg0/gC44Rcago+fW70nWzrOSsH+73jzqZmkePbklVf/zbWxf6QoUZYctn45k0nrgvn4ubM1H7dwrrn+Vq72CMD1cBlTvtjUQK10VooyTpGZ50LL5nEEOCmy96/i86nvscB9BB+9NYaO1cQI+pIcstNK8IuJIybQs+odq2BI3cDbLz3L7L1teWvWJB645DAvP2JbNcGvLIPkdD325j6XbCVL2vkbpwwGgrs0rXYNCY2zL+0GP0SCfxS63eV8O3sbVnsdPksXX2JiI1Grckgv1EOt1ifX/h54aDSaOi0Ad3kYOLl7N+nHbbQeGYW/R3XDEgPoct89dAkOwHzAzsoZ+2vZI1ZC6tFkKsqCaREZjMsV+sHpTx9iQ2o5Jr8mRAZ7Vj3iXutP34feYPl9LgSF+Z7Zz9OfMD9vvEu0aLQ1D7Jx8vAjLCwQrTmTnKIyzApqzKh7rsdNc+beuPIxiJnSjOMc+jWd0Pj+xIdWPbRM4xxCj7FvMedeJ/xCgtAAdjdfgiOCcCmpLmGChfKCXIrSXWk3oCMtI6oYR2U3UZybRZkeYuKaEOJ5JYbPCpBgQggBaN19CY8OxcuWSm5mEZUq7oIJwpai46z44jO+3JFF01uf5t4bOuKjKigvs6MspRSe2sFn70/lm4wYXu7b+kzeDY0T7r4hNPUFY0YqFuulUhQq7DYLpkojlmrqa1onZ1zd3XHWgjHvAEtmzeVHW38mvfAkg2PcSPHLZO24l5lbchXPvvM2o/vGXFApsFdmc3D9Vsp1wdzSpxln6r8anD38CY3z/2O/4mNsWLsLpy43cs81zesxlc+OzWrFVufhLgpD2m98OmkKP5YEcNOkFxh9lQ/6sjLsGg0Ysti2dAbPvv89Hg/Po6Pf2dexzpPIJmcq6zkWaxUTf+1YjQYM5moWR1ManN3dcXc592ooJ2P/MU5pgri+lYHvxg3l9W/y6PnEBJY/NZwWXtV9Qjb0BRlkn/aiZ5+rSAite0+JzVBMRuIBck64klNirqLc7rQZ0J/wqEn8sPUoTwyM4KJkV6V7+eaLtRTpbUQUF5BfaSXao4pJplpXAiITCACybRas9jqOcHdyJyIqluD845xOzsdIZJWZhGwmA0aLmeKULXy1aD2V5eUUrlrK4pXNGNW/Gb7OLri51m116YZgzd7Pqg3bOGg24FFWRIneSJSXRxWfgxvBzWIBG9k2C7VORGooICmlGH27QXRuHYibgx1pym7DZrM7NIzObspi248/cSAzD2vTcopL9Zjsvlx6broO7+CQ84b02MnZvYUNuQH0eegmesbVIj2umydhQaEEliSSm5pPqU3heakvV9nOrCBtKub01rWsXrObxJO5lP6ymjUdorihQxM83dxwcdX95cNLLCWZ7N24llWppXiG6SkuLkXFBF363tDo8AwI5Y9mBAO5R7fx2xEjV91+B1c3ryKPq81EaU4mOTZ/2vTqREwVPyBlr6AwPQ9LWRS927QiqO7tFaKBSDAhhABdIE27dSRozWF2HztCVnFH/H5fedlE+qHf2LDnJJXOkP7zNO77edqFx9vt2DUetBw8kjYRF7dUVd1KbiZ777dMeew1vsvnkr0BCjdiu9/Cc5Ne4do4C4mbV7HgtxyajhlEjygvwIhSWnTukXQfdAd3juj0pzG8isrcVHYlVuDVYhidYi7dlG4rOcZX773NZ5mteebth+hR07j6amhdvAmPjcPbr4ZJ3H9mL+fo+h/4MamCoEDF9o/G0Puj8z89hbKDLqgV13WOrWNupELWvzmOVxZtI19ziWE7SqEI5bo3P+WDuzqfqVCVF3L4dDq2+A50iO/KwOmLaNZjCv9+fT5Le3Tk5eLX4hYAACAASURBVMGx1VyfieKUU6Q6h3LdwA6EOVAhdnL1ITiqGV45UQR4V70uh1e3m3m672we/m4t+x8fQC+/815tllPMfvoNFhwyEh4Xi/2X/+OuB6zMX3QflyefkytR3foypMn3HN64m7Q7O9L8krdcBfu+eIOXP1hMotUZLYrouBhQu/ho/E18YPCk290v8M6r99DC9y+MJspOMn/6h8zdUUiT2GCylr3JGGdXvnrlNpr5NWDLb0E6Rwo0tB/ak7ZBXg5Wip3wCAgjukkg3m41ZSD6szL2fPEhU5fuQxcYhCZvNdNed8N50vOM7BhSQ+XIRt6ur3nzhUXoeo1m3KjuhHvWJhryJqZjJ7qHb2b//sOczutHROTFNWVVnMQ3n07hg/kbKLOemWMRFhMEB7/jrfu+ZYJLK65/4CmeHz+Y6NplBWgQyljArhWzeH3+dtyjQlDZa3l/kiu2V9/gng7+NRxtJGP7t7zz4VKyez3Kf+7rg38VRbcWZ7J/71FyozvzePe4Kr8LZSglKz2byqjmdOwUg8QSV5ASQgillLXkmFrw3J2q33VPqvm7c5StAc9dmb5DTRzbV/ld85D65kSFw+exGVLVign3qO6t71DTN2Yqq1JKWYvUrsVvqaGDbldvrk69+CB7hUpaN1M9cPdY9fYvGZc8r6XwiJr30u2qTfvb1JTVp5XZ4RJeedk/T1ThYYGq1yc7630uY9YW9dKIHqrLvbNUkuHM32zmJDXrprYqousktddQzcHFR9WnD12tYoe+on7Naci76dL0+2eo7pEt1C0zDjTYvZu1/j3Vxz9K3fLOBlVir8OB9gL14+Q7VVynu9W8PUUNVJrGzqqy1n2o+rm2UCNe+EllV7uvSR2e/4zq1mKkeueHE0p/+W+PBmRRWVu/UA/37ar6jJ6qtqaX1+1wU5b6cdp41fea+9WUn04qy+UpZONir1SpW+ersUMHqH53faR25Jqq2bdMHV01Rd3Ye7h6fMEBVfUjxqry9n2rHh05RA19/kt1Un8Zyi1qTSZgCyEAcPKNp++Ia4hzOs3a1TtIK2+4zBgajRatVotGW795h3ZzBSWZBdhcAggN98IJqEzbzw8Ll7Dd5Ep0+MXj061l2RzcepAySxP6tL94tLy9/DQ/fPo2762yMOr//o9Hh8b9rTOVa892OWjqnZ7URkn6MU4kWWnTrSvRZxtQtdgxG4zYysuorDJVlZETm5aycLOOO8ePpW/o5X/VeHa4k2nP9+HIvMn8Z2t2g5xTq9GeHadex5tWE0j/W25jkO04C7/eQOaVmsn/V9OeGQev0VS/4rQ1fSOffXmAsBvv4NaBcX+D1dzPURTs/YbJb8zgRMIt/N/r99O9rguzuYTTa/A19HHPZ9P36zlYXNO07b87Mzl7V/Le2/M4HnQtb749hqtCqurhslFwYAOLZm3BucvdjL+lfZVDBO36VH5duZbjptbce/d1JEi3xBX1t/kJC/FPZjfp0RtMXN6Eky5Ed72WMbd0xnx4OV/+vI+yBnrPOTlZMZYZsJtA1aNipdFqcXJ1wmzQU1Fpw1xwhNXLfuBoriJC54StMp/Ezds5nFJ4NrOKhcLUg2w5mo1Xr8F0Dr6wimPTp7Hruzn8Z62OB6d8zGsjWuCOlcydK/lhdzKmv2GGT2NJJcpqx1bvL89EcXISRzPKsPudG8JRxr4f5jLnVyOdbh9MmyrmFhftXcbHb28i6uYneeK6mHqWo7Z86DH+XT67O4Blbz/L05+sJ7W8Pl+gosJspNJsQm8Fax1ja4/mQ3n69Ztx2Tifzxbtx/A3vJfqxo7NVIHBbsFqt1cZTKjSE3z+0Xx2+vfloQeGEu/99wndjRlbmDNjGRlt7mXKh08yIN4XuzGHHd+t41Bqaa2zv/m27M9dY4cRnrGJxfM2kVlNitS/NztlKTuZN2claZG3MW3KEwxo4okyZrJnw0q2plX8sWdlFjtWzual91dS3PJ6XnzxxqrXL7IVsX/ZIr77pYL+d9/PyHY1DbESl5vThAkTJlzpQgghqleWvJnp095j5oZU3P3CiQz2xblhMn9eQKPzIrp5cyI0aaz+biUbkqxEJsQQ7uPIWGk7palbWfDSk7z48Res2bSPgvST7Nuyma1bcvFMaE58qEedWjS0Tm646Iyc2rOKRat+5ki5B03bdqNbS18Sf1rKT6dK8AiMpVWLWIK9XdFYSkncuJYNezRc+9g9dAo8b/StrYykn+fx+riJbMguQZ+zlxWLv2T+gjnM+mwpKVEDuemqmJqzrTQKlRz95iNee/FFpnz5EynZRWQd3smGdZs5fMqb9gMSqGP7KZiK2fHTEhbv1xOoP8EP/53NvAVzWHRE0fPup3nt8WE0+dNMVaVPY/O3HzP+g13EjrifF5+8lsjaLfXdQDyI7dqXzqGKtAN7OJGXi2dsO8LqlJXWQurGz3nkoeeYvWg1B7NzSDu5m21rV/FDbjh9ukTjo6vNXetMcOuOtA0qYf2sqSxOUcS3aEnE36jyXDtWkn/5hCdemsyM+T+wJzeDnNO7WLdyPUeN/rRuEYuvqxaUmexd3zNt2idstHXjycfu49q2wTVPWm4sTFmsmjWRV6b/RFZhLkc2/cg3C79m3mefMW9RFnFD+tIu2qd2E1E1roQ0a058QCX7ln/F8oPZeEY3JybAoxFk8mo4Sp/K6hnv89YHi0krL+DE9h9Z/PUC/jvzvyzZkExQr+voEeWOufgUa2csZUtyJVFX38KDY6+jpf8lfifKSnnGHr6eNZPpv1Qy6IEHeODmzvj+bW6i/10apS7XSkJCiIZiyD7E93On8MbnP5Nd6kXHLkO4bcwd3HBNN2L8XRq8i1FZykk+vJftW3aQ5xVDj2tuoEdUFZk3qj4LZn0eyQeOkG4EF1dXdNgwGS1onENo2q4ZkQHV5a+v4qy2SvJST3IyqxTXsGa0SQjDxVDI6aQkcm0+xDVrSoS3CxrAXHCchR99zJLsXnz82d3En/+mV1b0uekkJmZQqbVjNpqxKQANOjd/mrRoRUJIVRlsGhsrxcnHOHoyC4OzOx6uOuwWE5UGDX4hzWjbKZK6fnuWooNMe/Rh5hYN5+N3RuBRlI1eo8O7SUvaN43kovmmltMsfGoeydGhxPQcwvA+TalmOYfLTGHR55FdaMQzNIbA6lbovYgdfXYSOw+kolzdcHPToSwmjCYLhLWjV+swPOqUxtRI7rEt/PzbUZIPunD9W/fTyf9/KYWlHX3mMfYm5WLBCTfXs5+XQeEd05K2CRF4OltI2fIFb6wopU/HdvQd3I+WgXW9I68wq4HstNOk5BRjNpuxWmxneoo1Ojx8omnRJppAz6qTBFyagbyTe/l10wHSsr246sbBXNU2nP+Vu0NZK8lLTiY5qwATNixnM8lptC54BUbStEUcQe5O2M3l5KYUonwDCA31qSLAtFKUuINvPlhDxVVtadejH/3ahlU5DEr8tSSYEOLvQNmxmisozE5i58Zf+P6rb1i7/yTWqI5ce/P9jLnpajq0isSrgXO1200V6E02dO7eePw9muj/oAykbv6SCa8vwuPRT/nPqJZ/k8CgMVCUp67h5RufYX/3yXz32UiCazzETGluORpvb3wk3/vFLAZKio24BvpVs8ry/yqFubKEQpMLgT6euEhL8gXsVhOV5Sa07h641zkr1T+FwmY2Ul5sxMXfBw+5iRoVCSaEuAyU3Yy+tAy9wYqrly/eXmfWSLCbDZSVllJp1eLu64uvh6tDvQo2QylZJ3eyauEivv5qDcfLdCT0uY5R993JtT3aERfqg4vun/1KUvp01s6exrtrgpn81fP0CJQpYrVnIn3tVG68aQ7xny1nyT2trnSBhBBCNFLydhWigdkqCzm162dmv/MMwzp2pe/I11l5KJuygjT2/7aCGS+Npk+fXvR75EPWHMuv/SJP53Fy96VJu8E8MnkOa/bvYPWCl+nsnci0Z+7kmhvv5PFJc/hlTyK55cazQ3f+eUzGcorszrS8eQCtJZCoG5uNcr2VwEHDuKlrkytdGiGEEI2Y9EwI0aBsFKcdZOtRE536tCB/4RuMnXyY7o/cSc8QN3w69efajv7s+/wVbn5hMz2fmsyHzwwl0rUhehFsFKcfYfPyRfz3i0VsOOnF9Q+/yMvP3EK7YMcXYBNCCCGEqIo01wnRoJzwj+7E8GE9iPByxmpR6LR5HN97HFub3gzs0AQntLi7+RCKE04WG9YGi+ed8AkMJ75Fa5rHBYOpnLzcYirNjvR9CCGEEELUrFZZzIQQDrCUkJyTS3qpjoHdBzOgTQyeGkBZKdOXUBbqS3BCCH7O9YzprXoyD2/j+5XLWb5uJ2nFTkS3uIo3/jOEQQN70iqs6qwpu3btYuHChRgMhrovzCWEEEI0Ekop4uPjeeqpp9DppHr7V5JPW4jLpTiTQ6eyUF0HccPVHYk8u8yrvSybI4ePUunlT3R4AO4OxRI2DAVJ/Lr8e7758ge2J+WiwlvS89p7eHBgf3p3ak6Eb81Dmw4dOsTs2bPR6/WOFEIIIYRoNFq2bMn48eMlmPiLyactxGWiL8mjINdEXFwsMYHev48pNJZnkXKknKigfrSPDanTomh2cwGJu3/j+y/nsWTtVlLtQXToPZKnxw2mV4/2xIQF4lGHX/Wtt95Knz59sNv/55fnFUII8T/Ozc0NFxdJTf1Xk2BCiMvCROahPRw7oaPF4JaEB55bgtdCwfGDbDtaidd10YTXctE2c1EKW1d8zqTPFrP1YBEhkd24fswHTBk5kC7x4Xi7OZZz28fHBx8fH4eOFUIIIYSQYEKIy8FeTmZyOikuvnSNDsLnXLYmu5G8jBQyQ/zp37c1MT4aDAYjaJ1xc3WqIrBQVOYc4aeN2ymKHMI7z93NzYPaE+7nLhkUhBBCCHFFSTAhxGWgijPZd+I4+oh44poE4nGu1m81kZubi8EliNioEDQ5+1mxvZiEjl3pmODPpfsXNHhF9+KFD/vwpp8vbjJPWgghhBCNhAQTQlwG+qJ0Th8qISasJa2jgnA+t8HJlbCYeCL5hS2rlhCW04zY5m1IiPKtIpA4Q+flT8BfUG4hhLBZDBQmH+TXbbtJyTPi4tGU3sP60iEhQCoNQoiLyKJ1QlwGxvxjbFyxg7LQjvQb0I4wzz9CBWtZGtvX/cz2DCc69h9Mn7aRuNUwXsmQe5TVP61hx/HcWqZwtWOzehLdug/DRvSmaaAsWif+d1iyD7H1wGncO13PVaGOzRf6p7GWJfLLgmMEDelOh2ZhfzRw/FlFKivmfsIXP2fgGRVOqHsFh9fuoSCiO49OfJV7u0Sgk95RIcR5pJFBiMvALbgVwx5odcltOp9o+tw0hj51OF9l5n6WzJrMwm0FdTjKiaZ9niCmexcSAl1rNdFbiEbNks/WpTN4a2Exw+8dxjDP8wMJC8eWTOaVhfvROemwmQ1UGK2gAWWz4t/nTh4bNZI+LQOvWPFrosy5rJs5iY/WZeKuA7PJiNFydtFJJ2dihz/CEzf0pVWTuidN0Dp74KVLY+mna/k0ZAgvPz6cZt4XVwFspams37CFlLAH+WrSaFr66cj48XX63zOTj767juEdwgit79o4Qoj/KdIzIcTfgNVQQmZ6BrmlhloGBQpld8bLP5So6FB8HMz2JESjYUnjm9cf44O0jjw57j6u7RqP/wUdboryjJ18MWEc4+ck0uLmV/nw2T54W+xYipJY9+M3fPNrJcMmzGDqHW0aaUuamex9y5n43FNMX+/GTS+8xCPXN8PVaqMifTfLli1jc3Ys4959j/F9I+t+elVB6uHNLPjgS47RiUfefIi+sd4X7mPRk5Gdj80rjOgAdzRA5d4Z9Lvp/7DdPZPVE4ZLMCGEuEDjfJ4KIS6gc/cjprkfMVe6IEJcEWVsmfE6z26JYcrsp7mthd8l9tHgHdWNAd2bwaJsWl83kut6tjy7rS8DOvpx7If7+fyJqVzb7zOui2iMrz8Xwlv0oFubcDjlR8/h1zOkd8jZbf3oHGFj2NUTmT2pE/0WP0tH3zqeXuNJTLthPDNBy4Rx0/nPp9FEvH4zCd7nNTY4exEV7XXeQUVs/2kTJQHX8/pt3QmUQEII8SfyVBBCCNGolW3/gvFTUhg57lFuvGQg8fuepB1PxdO7Czf0ir1gizaqKZ3CXbDrT5CUbbi8Ba4HqzGPjKRy4uO70T3h/CFZWnxim9HOz0JxYTKZxWaH/w/3uGt49NEu5Py2kPnrj6Ovcs3KYrZ99iaPzzUy6rXx3NI2WFoghRAXkWBCCCFEI5bJyllfczqgPSP6x1c9cRjAnMnh7Vm4+7WlRaTbhdsKMziQa0Hr2YZWTTwuZ4HrpbIghcRjJkIjmhHhf+HwRHNuGodKXPENakZ0UH1W+dUSO2gkQyLyWLLoJ47mVl68i62AzdPf5JGpB+j7yks8f31bPGXmtRDiEiSYEEII0WjZTu9m5d5SWg8ZQIdAtxr3/f5gMd6D+9L+gjnKitRdW9lVaSF81HCuCmmsc4gs5B3czvp8HdH9OhJ9weWWcmDrVk64h9D6hr4096rqHLXkFUe/jmEYt+9g7+kiLuznKGHH3Pd55j8H6f74BN66szu+9YldhBD/0ySYEEII0UjZSdm/naNJ7vRs36rGCm1p1mlO6yIYMrQL5+radmMpx1dOYdyTc1Bt7ufdx/pT3UCpK8puIjsrC2NUGwZd1Yxzl2suSuXXma/x1Ltb6XDLIzx9c1vqn+zZm7iunQk3J7Jh50lKjefGOplJWf0VH355ioHPv8+URwcQ4gqVGbtZuvYYhZYqx0QJIf6hZPijEEKIRqqEpF1HOGUL5L64gBoq0HoO/boLfWU+26c+yqh5OhQmSkrdiAjzo+W9b/L0qFEMalH3tKq/s2Wz/tNPWHQoD1XbZMt2C5aooTz52I10DHavdldlzObojkPoc8qY/9Jo1nlpUcpAYUkALZt7MezFj7j9X8PpGNYw68YERjXBKziPLUcTKarsSbCbK4a0Lcx8912W7FJ0d5/MQ6t12Kxm8pIPktr6dX7u25zAaseaCSH+aSSYEEII0TgZS0jOzUbvFYWHR02vqxwObjyExqc/dz08mpYuYFd27LoA4lu3omVcMK61WvCxasqaz94lM5n9W1HdDox25oa7htEh2L3aEMRclsKhbVmEt/wX9zw0mAitHbtdgXsEbTo2JzbUr0Ff2m7e/gS7u5B3PJ1sg4UWuKLzb8Xdk+cxqNyE2WzGajubPd7Fg6CmHWgiaaaFEH8iwYQQQojGyWbFaDWDUlDD6Bp1chfLTpYSeOu9PHrbSOo7peBSNC4tGbt4LzdWWFDUcokmpVBu/kSG+9bQl2Gn8OAWfir3oMMz9zHmxs61eEErzqwUpcGROEnj7IqrTofdYMZss2MHnL3DaNMzjDZ1P50Q4h9KggkhhBCNVm3ryAWnj5NU6U3bjrG1CCTs6POT2brwAz44GcL4p17m+rhaDB3SuOAbEUNdl3eoHRMZiSfIc4mgZbOQal/Odn0uR/ZtZPkXC/jul73kWKxYW49i8htPcU/veFzqMBvy3LK1kqdJCOEomYAthBCicdJq0Tk5nanpVlvbLWP/mt+oKPGjc1xIdTti0+ew95elTHp+LDc+8RnrjhTi5NQIXoW2DHav3IWbLoKW4dWFK4rMLZ/z5KBxrPO5h2+OZZF+YAljDMt49pZX+DZRX4f/04pV2dG66HDSSjghhHBMI3iCCiGEEJfg7k98aAQeRiNGs63q/exZ7DucRoWKp2V09ROsy05tZPpmuPfFD3jjvjbYK82NolXeXniaXccKcfFsSnxYdRO1Fa7+zbn22Zd49YnrSfAAbXB3rh/RCWVP4mhGWa3/z9K8DLLKyglsEUW4u4tUCIQQDpFhTkIIIRopLyLjI4io2E5Wagnm7iFcKjusac8WViVlYg4YRER49cOV/DvcwX87AGUn+M5oaTTje7I2reXHPCuu3cMJDqju1awl5KpbePaq8/9WxOnj6bjF9aF7fG0HYSkK0lIozwvgqlZNCXCXFE1CCMdIQ4QQQohGypXo9p2IiSxhx5GTlFn/vF1RsG8pzz3zJjuTDVC0grcensya04ZanFvROCIJI8eWvcO/X5tFnlFPzs6ZTHh5LvvzL7rYKujZ+9UHfLDXnzGvjGdwvGctj6skL+k0RS5tuPqqFvh5SHVACOEY6ZkQQgjRaPl3G8LN/Wby8pqNHBt3DX1DL+ybcPGP57rx79H3SVecsWA0+RDp/XdKX6rFL6Y7oyfOZbTOCWUxo3FvQoBbbQKdcvZ8+RYPT9hFt5ff55nrm9d+MTtjOtv2Z6Dpdi1dmgU2wCJ4Qoh/KgkmhBBCNF5OCYwaewez71nKwnVJdL+zzXlDnTT4xHZmWGznK1jA+nIhvPNAbq7zJZSzd8FbPPTmZlo9MpGJd/eg2tFRF7CS/PO3fHfalxFPD6NtaG17M4QQ4mLSrymEEKJRCxw4mpdv8eXbGfP4NcvYQGfVcG6ok6ZRDHeqCwup6+bw4tTdtHl0Ih8+cQ2hLqBsFiz2mte/sObsZP7nuwnsehsPDGmHrzQrCiHqQYIJIYQQjVw4t709nYltd/HM89NYn1iAoZrkTtVRNiNF6Sc5uHsLR4/lQHoq+/fv41RKLmUmB0/6F6tI/InXn/+c3Lih3Ds4hor00yQe+YX3x9zAsytOV3OkheL07cyc8BkH6Mij40fSyl8mXgsh6kejlKrlMp5CCCHEFVSRzJKPJvD+On/GPDuKGwb1JKKOg/2tpXv4eNQY5ubp8HBxxkmjsFgqMZmu5v+WvsXIFt6Xp+wNppIj303n9UmLSHfSYLfZsaszK2G7B3bigXfe4cGuwRcdpUyFHFr+A4s2/cZR90FMePE2OgbKTAkhRP1JMCGEEOJvRGFI3sEvOxPx7nMXAyL/TpOtrxxr8UG++2gvwTcPpHv7GDyudIGEEP8zJJgQQgghhBBCOETmTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcIsGEEEIIIYQQwiESTAghhBBCCCEcorvSBfhHsaax6OUJfLZ2H3pnHRoAFFazCzGdBnPr2Lu4vnMCfu5OV6BwlWScPMKmLz/j858PUmRxIbTffbz80I30ah5chxvFSl7iIZJSk9i4eD7LD2ZhNITS4/rbuGfsSK6KCcTlgsuzk7lxLpM/mMv2bCNareZP51PYTFqajHiMSeNH0TbEtSEuVgghhBBCNAAJJv5C1uTdLN34E5sO5Fy07cCRHfz49Vyufvp9pr98G3E+f12nka3iFN9PeoG35qzlVJmBSqMVBbBrH8e37GTijLe5u2NQzSeyJ7NgzBj+b9V+Cqx2TBXlVJrtABw5/jMLft7BrFkTuL1zOC6/H5TP7jVrWLZqC9lVnjiK+BG+eHs51/NKhRBCCCFEQ5JhTn8ZCye2bePUoTLaP/klp4uMWC0WrDYz+ty9zB47nAhLOqs/nMN3O9Iw/0WlUvoTLHj2Ica9vY/YW6exKbkQk81K2ck1PDs4lNSDG1m69ghFtTiXKXEPPyVZaXrHK8z+divppUbstkoytnzBjZ0jMOxZwapfj1NmUL8fY8tM5Lcje8mOu41PNiVTYbViMZsxX/AvmcVvjSDGQ25XIYQQQojGRHom/irmDLZu3sFBY3Ne6NmRUH9Xzo328QzpxJjXnmFP2hFm/Hick6n5GK2xuFzub0eVs/vrKUycfYyer8xmxhvDCT/b+O8c15tbbx/E1I0bMefqMSngzyOQLmAmUx/GU/9dRMcWETj/vq8zkT0GcEP71mzauZ1ivQGr/dzJbGQc2c2h3wpoeW13ujcPw8PJCZyuxDAvIYQQQghRVxJM/EUMyQfZcvAw9k7/onunKDz+vIPOBSdnLRBOWJAvzpf9m1GUHF3F2x8sxzT8EZ4eP+z3QAIAjQYXFzfcFdjtClXlec5xIb5rH+IvtalST35lKWWEER8RhKvz2R4GawEHd2xhc3EYt3fsQmyQW0NcmBBCCCGE+IvIuJG/xP+3d5/xUVT7H8c/u+m9FyAkkITee5XeRBEQFMQKoig2bNhQRFQEERQFxYuiXq+IomChg/TeIQk19AQSUgjp2+b/AERMvCREE/H+v+/XKw+yMzt7ZncezHfO+Z1TwJFtm9i91Urbjm1pUMmv2B7nk45yNjEVU8M2tKwXjkd5N8maytpZ05mfEsYtgwfTPrRIb4Atj+RTJ8jxcsGvsh8eV+2VuLr0hK1s2HMQe62OdGleHd9LBROWlCNs3bSTwsat6XxjY0IUbUVERET+UXT7VhHyz7B163r2UIvRLRpRybvIdnsK6xd/z/I4d256owdNqvmWfExHDrsXTGfG4iOl6DWwY8kLou3t9zC4TwP8zHA+4Wem/Hsvles/wC0dYoqlSlv+afav3IFHUCw1GkbgX+qTLSInkR/n/oelpypxx+u3075W0KXhXTbO7t/B1o1JuHrv4KvXR7He1wXDgOB2A7ind0fqhJZ7pBIRERGRP0FhogLknI5j25o4nNsMoVWzqCK9DnkkfDuN8W+swrfrSB6/vT3hpZi0yH5+L3Nefpl/3unzOQAAIABJREFUJVhL1whzc0K638nFEUY57FvyPesKPOh68y20r1L8AwtP72fTzgLCYprSKjb06uUS/01hCsv/NYGxH+wk6paXeGRgW8LdLsUWSwY7N65gTZaFwqw4ls6N++19cxazvN0wXn1vFDfVDizbZ4uIiIhIuVOYKHcFnNq5kVV7TbR/sA2NI30uvW4jNW4t3/1nOi9O/YWAZsN4//2n6R57Za+ElVNr5/HZnExueWskja4YHWX2a8xTP8Uz3A4mUwm324aBw8Wb0LDQi7UauQdZOXcr3r5NublbA7yKvaGQozvWsNLwokbr9jSKLL5HiaznWD1zDA8+/z3OrR/k7VeH0qrSFTHKJZAuI2ewd8gUTIAJEyZTNkdWfMPE0e+wavW/mPxla5qP60246rFFRERErksKE+Ut/xzbt23iEOc5/MlQ6n7x4KUn7S64ugGeQbR5bjqTH+pP/V9vth0Wcs5ncTZuCe+8/xkHaj3Ho0XKLExOnoRF1yCsDE3K2ruGuUcs+DbsRoe6fzCAyXaSLT9uBK8qdOzRmMrXuLyDkX+ChVNf5qEJy/Hv9QxTpzxMt5iA3/cwmJzxDY7At8jyFTHRVXEkb2fTxJ1kHUkj1w4oTIiIiIhclxQmylluSjxbFu3FyT2YiKrBeJgNDByYTC24Z8xddG7ditYxgb9/U9oWxt/+LN/tP0iKS1NeeqIxAVdut2Zx4ngK+XZHyQ0wDEzuPoRWqkSAuxOQy77VqzmNmSb92lLLq3ivRl78GuavTcan0lB6Nq5yTRdJbtI2/j1pDE/NOUyT4eN5//m7aBp2DbM0mdzw9nbHydMDr+ggvBUkRERERK5bChPlykryrlXM3w+t7nuPOZ8OIbI0BQChNzBx9QaGzhnNkOmBdKkf8rvNKSsm0mrAJFLy7aVqRWDLO/ng85ncUdsLyOD4vlOYiaBZrcoUv81PY8Xsz9hU4EmHhwbQpmppi6CtnNs1n9efHMtXR1259YWPmDyyB+FupXz7rywn2br8EG4uMfTo2oAQhQkRERGR65bCRHmynGPHipUkB0Vyc7cmRFxDJbFReJRVP27C/6a3qOt/5Rtt5LtV564RI8gtNEooTnZgtXhSo3lfOkdfqnsoSOPYgXOYjeY0jQ4u9v78vT8xa/4uTDEjeKhbHbzMpWh0QRobv5/OuDGzORbUnrEfjmHoTbX/oBajZOfWL2TetmS8m9zLjQ3CNHexiIiIyHVMYaIcFabsZun3RwirNJCebapf041x/tEtLN0cyC1P1uH3M8k6U63LA0zuUsZG2QrJuWBgGGacnYs89s/exwcTprLMWp/7XxhKh+p+mABLShwLvp/HhjgPOt55B71aR+J56WTyM/bw1eQ3mfpdIlE9nubDZ+6mS6z/fw052Uf2cja0FjV8i3dZGGfWMmXSLHYGNefZ0bfRMPRauzVEREREpCLpwW+5sXN6+3IWnDMT3rQLLapfy+rOhRxet4LdMV3oWCew5N2vhYs73r4GhYWbWbBiDzmXXs49tYXJjz3KuHk2+j43gRd618PTCcDGqU3fM33MOKZ9vIyDmQWXrho7afvmM3bQQ0xb6cY9b8/m6w8eo+tVggRks+ebdxh5x+PMWLiT1MJfT/cCJ7fM5dGHR/HOWk+GPDOOJzrFoCghIiIicn1Tz0R5MZLZPH8VOf4RtO7TgqrXsliC7Rirv0ugQZ8HqeX1FxcNuMXQdcgNTHzxW+aPuZ19HwfjbraTn36KQ9YaDH1nGuOHd6aS56UGW1PYvmM3ezIgrH8X2jescnF62YJkfvnyfT5csRmr7yk+eyWeb14zsNuvWELPYSHPuw4DHn+FFwc1xPvCfhb/vIFfNiWyccsi3gsLxNPNjKMwj/NnT5Aa0Y0XZ73CI/1bEOKu1SVERERErncKE+XEOLWDxb+cJTzgVvq0jLym91r2b2D+4Zrc1aHO5eFEfx0f2jz2Nj+G1OP9l95kwd7TmKo24Pa73uHDId1pUa8qV07wVJiSSNz+eLKIpU+z5sSEXKyEyE9JYPOSeOxurjgKUjkUdwaH48q1uA0MA7zq1iC0ctDFoVq+DRnx3gzq793Ed29N5acDp7E4AmjUcSAPvTiAPh1aUqdqgGaCFREREfmHMBmGYZS8m1wzw3HxKb3JhNnJXIpVnG1kp54jI+88e2Y/w7BV9fh42jBaVatGZX/3clkF2nDYcRhcbKP5v7TRMHA4HBiAyWzGfHmBPAOH/eLrV/Vfjn35s4sdV0RERET+KRQmrheOs/z85uu8M3ctWe4euJntFJq9afHoNKbd2VD1AyIiIiJy3VGYEBERERGRMtFsTiIiIiIiUiYKEyIiIiIiUiYKEyIiIiIiUiYKEyIiIiIiUiYKEyIiIiIiUiYKEyIiIiIiUiYKEyIiIiIiUibOf3cD/n+5wL5Fy9h48DQFl1fFNnDYnAmMrEvzDi2JDfHC9W9aDLogJ51j21azPiGJXIcTPjXbcWObelT2dSn9QfKTWPPtUuIyc7D/0XkY4GSO4oYB3alf2fMP0qyV9LMnObRlAztOnsdqhYAazenaoSVV/XS5ioiIiFxPdHdWkVJ2MO3N0czacKzYJrfAUGLr92DIY6N4tF8zfCv0l8nmwMJPmTZrHstWbOdYTgEOTLj61KX30Id5/rlhtKrsUboj7f+R0U+NYmu65b/u4x50H//u0YH6eF7xqoWkvSuYNesrVm3by6Ht+zhjA3Cm0aDXqNO8BVX9/tRJioiIiMhfTGGiAp3esZn9iamE3TSKN+/tTLiXM5gMLBkH+fmjf/HV2i95+bQT1aLfZlDTEJwqolGOTDZ/9gajnvyC83V6MOz9x2kY6oUlaTP/mvQOC76YSUCD1tQd3gyfEg+WS9zKtZx2+NPqoWd5ulctvJzNFF1i3SmsPu1i/H7rlbCeZe3HExj/7rfEBTbjlp4P8PDoavi55JOw6TCm2BuIDr2G3hERERERqRAKExUmhW2rNrH/bAi39rubO25rym/P+m+iS70I8kY+xpxNa9m09xR9GoXgU+5pwsbJVbN56elPyevwDDPfe4KO0V6XNrXFKzOBJWN2cPZQCjkG+JQ0/Cr7IMsW7SU3twGD7ryb29qHldyEghMsmPA8o6fsJvb+sXz/+K00rhaCx6Wk0bWbBZsBbhWSrERERETkWihMVBAj6QCr9+0mo3I72jWNwK3Idt8qUQSHBQBOeLi4YK6A0vjClG3Mems626rdyAevj/wtSAA4uRFeORw/nDDsRfsW/tj5+E0sSzpLYas7aFXTBwO4av6wp7Hy/bE89dZ2Gj47gcmj+xPr+/vU4OLuivokRERERK5Pms2pQjg4uW8r+zacI7pbG5pFBRb74q3ZGeRmngf/htSrHY57eRdhO3LZO28GkzZA17tG0LeRf5HtVjIzM8gyg7ObC04ltiebPSvXczbZk+4DO9Mw2PPqQQIbpzd/y9QPl+PRZxijH72lWJAQERERkeubeiYqgj2N3Rs3sCEngnuaNSciqOjXnsPe9ctYvz6bRg/3pF3N4JLrJQw7GSfjiT9xvlhNwh/sjMPqRmh0DWKignAzQ2HKJmbPWIJbdDdu6dGYorXNjsIUDm/ahUt4ADEtoggs6SOyDrJifQJncgPpbjnN5nVrcTFMuPuGEVOrGsFerr/b3ZYRz9fvzmSte0vGjRxEXecz7N6WRFaeFbOTH5G1ahIV4vlfPkxERERErgcKExXAevYQmzZvw1KjPS3axBLwu60OsvYuZsa0eRwN78nUfp2ILLE4ARy5CXxyXwdGr84qZStiGTZxFm+P7ogbVo4s+4rPDtmpP7w/Per4Ftvbkn6YHauTCAy4mYbVgku8UJK3rGRp3GHyyWfms4OZeel1r6DqdLznKZ4ccQedagVdOk4hxzbN5z+LEwmKrkbi4uk88uoP/LQ1kexCMDn50Kz/wzz8+MPc0b4aHn/TVLkiIiIicnUKE+XOxpmELWxYkUPzYb3oVD/08hYjJ4ntq7/j9XHvsPpUDV6YNpZ7u1TDFQupx46wd/1P/LA5CY9K9blp0EBuqHHF8CiTF/VueZbXOjtKKEwAHFYK/evSu1+Ti0HGdpL181ZjBFSnU7+2VCl2FdhJjdvC8ixPQtt0oHWNgOLHLLK/OaQxI554iZvz7JhMJkw4yDubwJLFS1k09RVyHJ5Uevlu6gW54Mg5yvJPv2FPgZXA3CSOHPchttVgnu7qjinvNOuXfM/yee/xZoYL1T8fQ+cI92v4vkVERESkoihMlDdrJnu3b2CzkU/Y+tk8f/9KPE0GBlYyTxwk7vBRrHXuYvJnTzC4V218TAYnF01h6nYX6kaG0qC+O6d2LOK1lw7y6LhX6F/n4oAks1c0vZ98id5laJLl6CbmbkzDr9IAejcNL76DkcrOJas46xNE/96tqO5dUmmNE+FNenJ/k56/f9mRTtcZL/PYYx+yLX4PxzIGUi/IhexD6/h6dTKh9e7mtenPcXuLSPzcXC9lIguH2lVi8OC3OJEUx+HkHIUJERERkeuUwkQ5s2QeYfvKrdiwknRwHfMPXtpgiqLX8EE8+mBn+vZqT41Q74u9DvZkEgvr0ndAI5rWisLXGc6sMdj12kr2p+ZeDhN/okUcWLWUnVaIvqkLjUKKXwKO5F0sWngEX/eu3NiqOqVbru4PmAOpVacGsd5wzs0TNycnII/961ay1+RJ0wGDGdQ+tki9hitV6zWkjosryU4euLmqKFtERETkeqUwUa7spCds4OeVWdTvPo6p799PXW8Du8MA3PEP8cfT3eX3xdbmMNrc1Ac3FxMmE0AOp+KOk1kQRnToxWXj8vd9zq13vcnetBxMpquNcTKwFxr4txjI+ClvMLC2D5BJ4s5EHARTv3EU/sXek8+OebNZmFFA5Ijb6VqjeD1F6eVy9ugx9ueE06JGY2JCvcBxhN0Lt+HsXIXWzavj/QdtPn9iPzsc7gRFtaJh9ZKXyhMRERGRv4fCRHmyX2Df+hXs8Ajn1t49aVerSslP+U3OuF8x8VHO4dV8+vN+Im57iV51fAAridu3cPz8eQoK7Fw1S+CgMN+H2n51qF3l0k25NY3E+CScjFq0q1W5eLlF2lbmfLGGfJdO3NevBSFuZZ892Dh/kg2bN3AkpDa33dCQSt5A2iniDhXgZIqgRrh/8bmJjTS2L1vJaXdPevRpTQ1fXaIiIiIi1yvdqZUj24WDrP1uNz5VWtOtQ81rHi5kSd7Kx69/zhHvm3n73vaXehFcqD90BvuHzihbowpzSDtrxyCAAG/XIhszWT5zCp8cNWjw9APc2igEJ8AozOL06dOkZpgIqRZF5WAvnE1gtxZiKQAPn6JL8AEUEL/sS76Yc5TWt73KwBtq4AHYM1I4YLNhNZkwm52KhBkHZ9Z9xdSZewmLvpuhXevirZmcRERERK5bWrSuHGXuW8O83XlUju5E+9olzYhU5L0HlzDu1emssrTjtekjaBLwF/1UZjNOLjastkPsSzx3+WWjMJOts8fzyOTNhPYZy+ShHQlxvzgAK3Xrf3ikZ2Oat7yL6UsPk+cAsHPuwC+898K7LNh0glzrb6tdGLlpxC+cwYtvfEZysyE8/vjgy7UZTr5BxLg4sOSfZNfBk2QblxtA6s6veeWF94gP68Sodx6nZ80/M8RKRERERMqbeibKTSZ7li3joF8Qt9zcnpqlXn8tj/iFM3l33g6y6w3hg0d7E/VXTmbkWYUmN0RRcGgH08c9j2NPC0KdC0jet5LPfzxAaKcneH/8fbSq8usK1hnEbd1BQqINl/adaNcyCh8nwJFL4tpvmDT9M8yLFtFnQCdqV/LHxZbB4R3bWb9iG4XNb2Xcy88yoFnYb3UhQXXoeVMDvpq+ni/GPUNhXCdqBZpJP5nAuvkbSA5vzbOTXmJoh2oU7TcRERERkeuLyTCMkhdQlmuXsoT7G/djrrkbU5d8xQMNSvGU3ZbEmvc/Y/6eZEJuHcojvZvjXw5xL/voEiZOmMG8WT9xEMAtmFqNO3DjkCEMvfNmGgb9NmzJfmYjr4y4mzd/OkOXZ2Yx8+XBxPqawcjj4Lpv+eSLn1m1bg3bD13q5XCKom3P1rS+oS93DLqZ5n9QQJ2fvIkZE6bw5Zx57E4H8KdGk1Z06jWAwXcNoFPdQHWZiYiIiPwDKEyUEyM3me0bEsh1r0qjNrUIcLn6/tbkLXwwexFnzfW46+4+1I/wKHEtuj/DmpXMoT3xnMqzYvIOpnp0XWLCvHEqMhOrPS+NQwn7OJriQvV69YiNCsD114Y5HNhyMzl18hhHT6djMxwYAVE0iI0gLMj3qt1e9pxUjuw/wIn0XGweQVSLjqVGlQBczCqSEBEREfmnUJi4LpxnzTvPcs8zn5FRqSoRAe5gs2O1QUD1Xrz04QT61Sj1OCkRERERkQqhMHFdcGDNyyOvwILdcFxah+Iik5Mb3n4+uDvpib2IiIiIXF8UJkREREREpExU5yoiIiIiImWiMCEiIiIiImWiMCEiIiIiImWiMCEiIiIiImWiMCEiIiIiImWiMCEiIiIiImVytUWK5f8jw4G1IJuMrDxsdnBx9cY/0BvXMq5zYdgtZF/IIiffgmG44hPgj6/nHywHbssn/fwFCi32/3YkDCcvAgJ98XAxl+vq4CIiIiJSOuqZkMsKLxxj+VczGDO0K5UrVyYiIpK6Tfsz5tMlHMy0XNvBjAJOJ6zh8zefoGfbulSpEkFERBNuHTGeuTuTKHD8fvfMLZ/Qr20NqlSp8l/+Ioi4eTy/nLiAFkYRERERuT6oZ0IAyDm2luljn2PC0iSiajWg34DbMBWmkbBlLW8/cY7z1g+Z+FBbAkoTPx1Z7PluOs+/PoOtuWE0rdOBQY0gM3E3q+eMZ2uiQfjc5+hQ1ftSD8N5dv2ymZTjztRo0Z16kX64mK/sezBwWF2J6d+P5pV8lIBFRERErhMKE4KRsoOZLz3Jm+vcGPbGZzx3TyfCXc1gyWT5lMcY8sJCDu3cz5nsNgT4lTTAyMaJxTMY9fz7JNe5h0/eeJzejargChTEz+O+gU/w065N7D6eTdsIb1xMQMYhVm7bzWHndsyY9hkPtw6qgLMWERERkT9LD3n/v3NksOI/U5j4cy43PzeRV4d1uRgkAFx9qNmkMQ3NBg67FYe95AFGlpMrePPNmez0v5Xxk1+g36UgAeBerxEdIgPwdrJgs/02zil131Z27T9NQI92NKrqXg4nKSIiIiLlQWHi/zWDtO0LmPnWEvw6DePxQW3w+90V4cCWX0iBAwwMSq56TmHp5Cn8tCOIu0Y9xE21/H+/ubCQvHwHdruBYfr1eOnsWbuZhCN+9GzbjOhAz7/yBEVERESkHGmYU4UyyDt7kE1rV7Nh1zEu2MwE1+nM7QO7E+2bw4EN69gaf5pCkxPhzW/ixkbhOJdn3Cs8w8r5X/BdVhVG9O9Jo5Ail4M9hxMnDrKPYHpWjSLA++qNyd71M+8u30F640cY1D0GzyLhw5F0mM2Z2RQGtSemis/Fi+9cImv37eREaGNGtahBoIfmaRIRERH5p1DPREWxnGP7169xc6fO3HzvGyzee4qUA6uY9MT9PDzxJxIPbuTDcU9y74gRPPvpTgrdXDGbActZ5o8fiL+TC27uHnh4XO3PDVenMLq/tZT0gpKGJBlkHljO1x9sIKxlV/p0rEnRAUZG7jkOJewhOzCYsNjK+P/BjK6/OcPSWXPYf9ibvnd2p36Ae7GOjOQDuzlyIQujXk2qBrpjwiApbit7tx2H1B95pmct/DzccfPwpUrTEXy0aBfnrJq7SUREROR6pZ6JCmDkHWfBpOd4esI3HHNqxiPvvse4+9vhk76Mp5r1Z/68D3jtkMHq5YcwRQ9iwtvP07duIGbAlnOOMynnCYqujrNTSesr2LAWNqVvi1p4u5XwhN/IJ3HLShbmhNKldXdaRnsU2yUnLZG4dWcIrdmX9k2j8Lra8U7tZXFcImeDO9K/Y2183Irm1PMc2BxHVqovXZ9qSbS3G5DN2ZPpWNwjqVnr11FUBta8TFL2fMxjd+9i/4RpvDq8delmkRIRERGRCqUwUd5sGWyY/SbPv/UNxywhdHv2GZ65ux1BzmBxdsfH00byoeV8cQicI3sy7t2x3Ne+Kr92AjgHNmDkBysY+Rc3y5F/kk0/r8FetTotezYipNgeFpJ3rWPRfjOxd7elSTX/PzjK5aNxdNtGDhxOIbJPOxpV8aVYJ0bGfpZuiCeJpjzXIgZvVwAfmt07lsX3jv3drvkn1/HR2KcZ/VkCaxas5lDf5rQK06UqIiIicr3R895y5eDszu94ffKXHCoEl3o9GDKwK9Uu1Rg7ci6QnnVpGI9XA+4b9RwP31iH4n0Ef72cIxtYsOIc1SI70aNRpeI7FJxj95rVHAurTJ2uTYn0vkpPh+M0m1Zv5tCZavRt14zKvm7Fdjm3ezPbTpzBrUcHmsUG4nqVw3lEtqbfgJ6E44TdYcHm0FAnERERkeuRHveWI6PgBMtmfsjS4/lAEO079KJjw9/6AArOHGNXhg0IotVdT/DU8E4EX/GLWM8f4Of3P2X9eQemkuqSHRby/Zoy+N7+tK/mV8JwKCtn4rexzupNwzpNiAkpnikvJK7iyx+PERl9J/07xl494KSeYNvx46QFNaJOzXA8i3ZLOE6xdNFy9p2oypAxNxAbUML0r4aNzIw0stx8iI6uSaWAqxZriIiIiMjfRGGiHOUe3ch3P8Rf/Ce4Bk06Nue3ZRQy2L5sKQk2A6rUpGPf7tTxuTICODi7eQ5Pv/I2x0r5eaa2I+k2+NaSZ3Alj6TDidh83YhoFUto0c1GJjsXfMe6LH96PnwL7aKuWi1BTtpZ0lLO4dS0GtXDfS6vK3F5+771LFy5G0eLuxnQvjb+LldvoVFwhj3LN2EJjaR59yZEaukJERERkeuSwkS5sXBy13pWZ9kACIiuTbOGEZdqCQzSNs1l0sw15AGBAZHUjwws8n4zoa2GM39nfxxQYs+E4QD34CpUr+xbirblkZaUhrtLKLERATgV2Zq77zvemL0Fz+p3MnRIc/xL+Oy8rDRyMgyq1q5EqGeRIU6WU/z4zRx+jAvj3un9aFa9pF4TO6c3fMMHi5OJbDKKATdU10UqIiIicp3SfVq5sZGZmkSByQG4USUohhrh3gAUnFzOpKcmsDY5B3DGz6sKVYOKP/13C6hKo4Cq5dA2A4fNjsWexbkLhb/fkr6Jtx4bz470+jw+5X46RfzarnMsevExxny2jJSWY5j/r4doEeKJCXA4wCCX9Owscm1X1jfksu/76bz77jbqDX6Z4X2bEOQEeYnL+E9CJfp3r0uw+5VRxiA7cTHvPD+dE+4dGfPiEFoGF+3nEBEREZHrhQqwy40JJ7MTZhuAg/z885xLSyd134+89tjb7PeKxNv14o203ZJN0v4d7Io/Qlp+RRQbexJcNQxTWjJrZ3zCj3uOk3L2BHtWf85jPQczY08od0wax6O9auL+azfCmX0s3L6HXWds1Gxehygv18s9DJ7+gfgH+pD9w3z+PX81CcdPcfLYTr6dMIrhD8wkv/P9vPbyEJqGuQP5JCz7kmfu60iXRyaxYH0cp5KSOX1gCz989iq3d3iQOUmxjHhnDMM6VlPaFREREbmeGVJOHEbqxhlG1yq+BmDg7mdENGhiNIppZQx88VsjYcts44YAl4vbwDCZg4w2Iz4wdqZaKqR1mXu/Nu5rEG0EenCpDSbD0z/IqF5niDFxQbyRYXVcsbfdOPLdy0bLMJMBnY3JS48aefYrNltOGfNfHW60CPUwzKZfz8fZ8A+va/S8e5qx9liWcflojjPGwhcHGo1rVr3iszFw8TaCw6sZDbo9ZXy+9ZRRMd+CiIiIiPwZJsMwNO9meXGcZ/f3s/jg8xUcy7OAbyzd73qIh25pir89kW/GvsbsnUkYvlVpc/Pd3N23LdGBFVVtbJB9chvz585h0fbT2N1i6XLLTfS+sSWRV/Q6XJRHwvzZzJ73CyerD+CFJ/rR6NIQp8sKM0lY9x2ffL+BpEwrPhGdGHJfV5rVqY5vkf4vR0E6Rw4fYsfKBSzadgqr1YlKzbpwU6fOtGkWiZezOsxERERE/gkUJsqdgd1eQF4euLm743rFTEaGYScvtxCzmycemv1URERERP5hFCZERERERKRMNJ5ERERERETKRGFCRERERETKRGFCRERERETKRGFCRERERETKRGFCRERERETKRGFCRERERETKRGFCRERERETKRGFCRERERETKRGFCRERERETKRGHib2TYLeRknOFYYiKJR49y6kw6uYWOv609eUnxrPx2IrfFRhLi7UtItVYMn/I9cUm513Qca14q+9YsZM7M8QxuE42fnzeeno24/fF3WRJ/lgJ7OZ2AiIiIiFQok2EYxt/diP9vjMJ0EtYvY/53C1i0djOJKTnkZmeSWxhEk/6P8tZbj9C1ZjBOFdaifI4tn8XIJyazPjmfAD9PzCYHBVmppJz3os29b/LR1PtpGOBc8qGyt/Jy1wG8t/8C7l4+eLi7YMZOQU4WaRkXsNV5gDlfv8HA+iE4m8r/zERERESk/KhnooJZz8XznwmP0uemYbz/i4n+r/6blQu/5Jm+LXEnjV3zX+exd37k6AVbBbXIwvHlH3Lvg+PZENyLtxdt4+iJ4xw/fpQt37xOD78c9u5bxZbEzFIdLXXPHg4Ht2P4xC9Ysn4Px44f49jxE+xdPI3ejSIwJSxjxfaT5FuVYUVERET+6UrxqFn+Mufjmf36U4z+cBk5lQbx8ZzpDGsSBKRzsn4sft9soQAbh44lk1FopSJ+nrxjq5ky9j12+vbl048mcXtdv0tbnKnSpBvdOr3PimNZXCgsTbgpJM+7GW98fhcxIR5XvG4ipHErOkZUZs3uU+QXWnEoS4iIiIj84ylMVJh0Vn46lQmfLCPLWon+o0ZuDSxiAAAHw0lEQVQyoHHQxU3Z6RxJPkEKAK40axRLuKdb+TfJksLKL97jP/sCefj9R7nlcpC4yDA5MMxgGAaOUo2Gc6Na46Z/vCktmSM5GVwwx1AvOhRXF41xEhEREfmn0zCnCpK+7Wc++eQ7jucC0b25s3dD/EwABqkHt7BhzV4ATFE3MfyWdlTxKu+fxiAjfikfTd2AX5t+9LupAe5F9shPP8nRfVmE+UYSFej1Jz7LxuGNa9h+MJWQbr3p0iAcd115IiIiIv946pmoCNYzrP75RxYnnAe8aD2gBy0j/QGwZcYx918zWbD/AqbgZowc8xQDW1e9+MMYBST8/B5PvjGfCyYzpqs+zDewFzoRPfg53n64NxHeJZRvW1NZN3cWS/IDGHxjdxqFFL27t5KeuI/VR8xUrl2T6BDvMp++7eQ6Pv5iLttozWuj+tEgzBP1S4iIiIj88ylMVID8pHg2rtvCeQAa07NtXZzPJbB2+xq+nvUBny4/SWjL+3h+3FPc2bU+/i4X32cUnGHrwrks27KrlJ9Unbr3+OPhXvI8UJa0PSz5ajsBNQbSp1djikUFWy6J8bs56BPMza2aEBVctrml7Gm7mT7+VT74MY+bXxrJPR1qUu6dLiIiIiJSIRQmyp2FpL2b2bwhCQCn9i1pVdfKkpdGMvrbLaQVulK584O8PflFbmsa/rvpYE0eUdw1bROD3y1ttbIZZxcXnEu873dwdscvfJ/sTkTrzrSs6VlsD2vOEbYu3Il/pXp07FiHkDJ0JdjO7eKjl57kxVnHaPfIa7w16kaivCpuwlsRERERKV96RlzeCtPZs30TWywA/nRq1Yq6lWvS/elXeeG+rgQ4F5K86iOefGgcX29L4fexwYyzqxvu7u6l/HPF2akUd/2OVLYsXE6mfzCN+ram+h/c3184sIkftmQTWqkN7WuHXPNpZ59Yx6QnH+P5r9PoNWYKM964k3rBrtd8HBERERG5fqlnopzlnzvAlmXbsAO416Nl8waEenvh0bgnI192cHDJBj4+kc/ZbT/y7Tc96dSkH1WcAft5Dqxez4HsUkzJatixuARTt2lT6lb2KTkhZu5n9c+H8fW4kR7NoorXLzjOsfWHhcR5BNOrXydqB7tcwxlbOLHxGyaOe52Pj4TywJTpjL2rA+HuqpIQERER+V+jMFGubJyN28DS7ecA8G3VmBZNI/l1BQYnLz/CfH79CSwUFuZjtQHOkHfgex7sOZx19lIOcYrqzRuffET9yj4l7lp45jh7s5xwr16XmEpF53ACy/FfmP3VFryr3cmwvo3xL20OsKez6+t3ee6lf3GoUgfenP4aj/eqXWyWKBERERH536AwUZ6s6exdu5o4O0AorRq0o3Hkb6XOljPHiUsquPiPdxQxTWoTeunO2+zXkOEzPuIOu72EDzFw2Ez4RDemS6vKpWpWdmoSh83gUTeWKn5F+zHSWDZlBmvSguj09B20iSheT/FH7Cn7mD11PBM/2k7lvk/yyejhdKkXpFmbRERERP6HKUyUI0t6HKvnb8cB4BVBTJO6/NYRkEvcsp9ZdcEGmIloegM9O9Ti11t394jm3PNg83Jpl91mId9h4J5nu9i2yxwkfv8eY7/cS6VeL/DUoGYEupiAfBK+mcH7n//EgYjbef2Vu2lT5dfhVHZS9v3AhDFv8cOJcAZNnMUDgzsS46dCaxEREZH/dSrALjcOUnat5Ydj2Rf/rRpM9VqVLg/5ydgxl7cmL+S83cA1vD13j7yf7jGl6wX4s9y9fQk1LKRvX8BPG5IvFn3np7Dx388z5KkpHG32AGPHDaNZ2KXW5h5n6eL5fLpoDYkFBj4uLpcunDz2/zSJ+/uMZq2lG29/+iGvj+iiICEiIiLy/4R6JspNKjuXriTJdunZ/+mjrP3pB5pQC8vBX5gxYRpLTufgE96ZkW9P4rnb61dYbYFvg+481O1zXln4E6M6L+F5V2ecnJ1wWFyp2ed55r35OB1j/C5fHOcP72J7fDwW6tDvhhZUDbzYUmtqPPM//4yFJxIxJU3hvvUfYMKBcWWZh62Q/Mo9ePm9KTzXp9blehERERER+edTmCgnxtmdLPwxDpsdzK5eBPlZif/qNYZ+aYABZpcY+j42hOHDBtG1cWXcKrBtJp9GjJo5h4h/z+TtGQtJLfSkXpf+DB5+J/1a1MTP48oOq0JSjp7GludF5S69uaFVdXwvLs9NdspJziWbiY2JwTABxWrFDeyFBsGtm9GqdiUFCREREZH/MSbDMEq7IpqUmsGp+U/R7t7pnMq2U6nxQ3z0/Vv08M/l3IVCDFzwDgkl0FNZTkRERET+uXQ3Wy7OsG7eL2TmWQF/KjW5gaZRPribfaga8He3TURERETkr6EC7PJwaitz15wg1w7mwAja9GlOhL5pEREREfkfo1vcv5yDQysXsiXrAgYmAvw60LtF1b+7USIiIiIifzkNc/rLZXPeuR73PfA4BbgSXOdW2kVUZHm1iIiIiEjFUAH2X86BtcCCAxOYwOTkgquzOoBERERE5H+PwoSIiIiIiJSJHpmLiIiIiEiZKEyIiIiIiEiZKEyIiIiIiEiZKEyIiIiIiEiZKEyIiIiIiEiZKEyIiIiIiEiZKEyIiIiIiEiZKEyIiIiIiEiZKEyIiIiIiEiZ/B8iGAD56KuoZwAAAABJRU5ErkJggg==)
Formula 1. Sample size formula
In another way, in the next step, by using the G-Power software the sample size in each group, was calculated based on repetitive measurements (6 repetitions), the effect size of 0.5, error type I (α = 0.05), and statistical power of 95% (N = 43).
Participants were allocated to two groups by permuted block randomization method in which the block size was assumed four. Also, Allocation concealment was ensured, by the central telephone method and assigning 5 digit code to participants in each group by a person who was not aware of the hypothesis. Nurses in the operating room were not blinded. On the other hand, data collectors and outcome assessors were blinded to the group of participants. Patients were blind to the group they were in.
In each group, surgery was performed by the same surgeon and the same method. Patients with the following conditions were excluded from the study; patients who reported addiction to psychotropic drugs or substance abuse, had a history of allergic reactions to drugs used in this study, and who needed a different surgical method.
Before induction of anesthesia, standard monitoring including noninvasive blood pressure measurement, heart rate, oxygen saturation (SPO2), and electrocardiography (ECG) was measured for each patient.
Patients were in the supine and sniffing positions. They were pre-medicated with Midazolam 0.03 mg/kg of Total Body Weight (TBW) and fentanyl 4 µl/kg of Ideal Body Weight (IBW). Anesthesia was induced with Propofol IV, 2 mg/kg then Atracurium IV, 0.2 mg/kg. After ventilation with a bag mask, an experienced anesthesiologist performed laryngoscopy and tracheal intubation with a proper cuff. Anesthesia was maintained with Propofol IV, 50–75 µ/kg/min of TBW, Remifentanil IV, 0.1-5 µ/kg/min of IBW, and Sevoflurane inhalation gas (1-1.5%). The doses of drugs were considered as much as the patient’s vital signs remained utmost %20 of their baseline. Surgery was taken 1.5-2 hours for each patient. 45 minutes before the end of the surgery, Fentanyl 1.5 µ/kg of IBW and Ondansetron IV, 4 mg were injected. By the completion of the surgery, muscle relaxants were reserved by Neostigmine IV, 0.05 mg/kg, and Atropine IV, 0.02 mg/kg of IBW. Immediately after extubation, patients got IV Pethidine 0.5 mg/kg of IBW and Ondansetron IV, 4 mg.
After all, patients in both groups received two different regimes for controlling post-operative pain and the VAS score was compared for each regime. In the first group (Group K), patients received PCA infusion of 0.4 mg Morphine sulfate per hour, Paracetamol 1 g IV every 12 hours, Ketorolac 30 mg IV and 30 mg IM, then 15 mg Ketorolac IV every 6 hours which would be the total dose of 120 mg per day (24)
At the same time, patients in the second group (Group M) received a PCA infusion of 0.8 mg Morphine sulfate per hour and Paracetamol 1 g IV every 12 hours.
In the end, the patient’s pain was scored using the VAS scale immediately after the surgery, 6, 12, and 24 hours later, and at the time of discharge. Patients were trained for VAS score measurement before surgery.
If the VAS score was more than 49 mm the patient received Paracetamol 1 g IV with a maximum dose of 4 g daily. If the patient felt nausea, Ondansetron 4 mg IV was administered in both groups. Also, we recorded the frequency of Paracetamol and Ondansetron administration.
A repeated measures ANOVA test was used to detect any significant changes between groups in their mean scores of pain after the surgery, 6, and 12, 24 hours later, and at the time of discharge.
Data analysis was performed using SPSS, version 24. Data were analyzed using descriptive analysis, Chi-square test, Student T-Test, Paired T-Test, and Repeated Measures ANOVA. The significance level for alpha was considered at 0.05. (P value = 0.05)