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10.1097/j.pain.0000000000000429 http://scihub22266oqcxt.onion/10.1097/j.pain.0000000000000429 C4979073!4979073!26588689     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pain 2016 ; 157 (3): 717-28 Nephropedia Template TP {{tp|p=26588689|t=2016. Markov Chain evaluation of acute postoperative pain transition states |pdf=|usr=}}{{26588689}} gab.com Text Markov Chain evaluation of acute postoperative pain transition states JO: Pain http://www.kidney.de/pget.php?&tw=1&pmid=26588689 #FOAvip Prior investigations on acute postoperative pain dynamicity have focused on daily pain assessments, and so were unable to examine intra-day variations in acute pain intensity. We analyzed 476,108 postoperative acute pain intensity ratings clinically documented on postoperative days 1 to 7 from 8,346 surgical patients using Markov Chain modeling to describe how patients are likely to transition from one pain state to another in a probabilistic fashion. The Markov Chain was found to be irreducible and positive recurrent, with no absorbing states. Transition probabilities ranged from 0.0031 for the transition from state 10 to state 1, to 0.69 for the transition from state zero to state zero. The greatest density of transitions was noted in the diagonal region of the transition matrix, suggesting that patients were generally most likely to transition to the same pain state as their current state. There were also slightly increased probability densities in transitioning to a state of asleep or zero from the current state. Examination of the number of steps required to traverse from a particular first pain score to a target state suggested that overall, fewer steps were required to reach a state of zero (range 6.1?8.8 steps) or asleep (range 9.1?11) than were required to reach a mild pain intensity state. Our results suggest that Markov Chains are a feasible method for describing probabilistic postoperative pain trajectories, pointing toward the possibility of using Markov decision processes to model sequential interactions between pain intensity ratings and postoperative analgesic interventions. Twit Text FOAVip Markov Chain evaluation of acute postoperative pain transition states ????Pain http://www.kidney.de/pget.php?&tw=1&pmid=26588689 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26588689 #FOAvip English Wikipedia <ref>{{Cite pmid|26588689}}</ref> Markov Chain evaluation of acute postoperative pain transition states #MMPMID26588689 Tighe PJ; Bzdega M; Fillingim RB; Rashidi P; Aytug H Pain 2016[Mar]; 157 (3): 717-28 PMID26588689show ga Prior investigations on acute postoperative pain dynamicity have focused on daily pain assessments, and so were unable to examine intra-day variations in acute pain intensity. We analyzed 476,108 postoperative acute pain intensity ratings clinically documented on postoperative days 1 to 7 from 8,346 surgical patients using Markov Chain modeling to describe how patients are likely to transition from one pain state to another in a probabilistic fashion. The Markov Chain was found to be irreducible and positive recurrent, with no absorbing states. Transition probabilities ranged from 0.0031 for the transition from state 10 to state 1, to 0.69 for the transition from state zero to state zero. The greatest density of transitions was noted in the diagonal region of the transition matrix, suggesting that patients were generally most likely to transition to the same pain state as their current state. There were also slightly increased probability densities in transitioning to a state of asleep or zero from the current state. Examination of the number of steps required to traverse from a particular first pain score to a target state suggested that overall, fewer steps were required to reach a state of zero (range 6.1?8.8 steps) or asleep (range 9.1?11) than were required to reach a mild pain intensity state. Our results suggest that Markov Chains are a feasible method for describing probabilistic postoperative pain trajectories, pointing toward the possibility of using Markov decision processes to model sequential interactions between pain intensity ratings and postoperative analgesic interventions. äMarkov Chain evaluation of acute postoperative pain transition states (epmc).pdf DeepDyve Pubget Overpricing