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Mani Menon - One of the best experts on this subject based on the ideXlab platform.
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application of the statistical process control method for prospective patient safety monitoring during the Learning Phase robotic kidney transplantation with regional hypothermia ideal Phase 2a b
European Urology, 2014Co-Authors: Akshay Sood, Khurshid R Ghani, Rajesh Ahlawat, Pranjal Modi, Ronney Abaza, Wooju Jeong, Jesse D Sammon, Mireya Diaz, Vijay Kher, Mani MenonAbstract:Abstract Background Traditional evaluation of the Learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. Objectives To prospectively monitor patient safety during the Learning Phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). Design, setting and participants From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience ( n =7); group 2, robot trained and kidney transplant experienced ( n =20); and group 3, kidney transplant experienced with limited robot training ( n =14). Intervention We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcome measurements and statistical analysis Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. Results and limitations CUSUM analysis revealed an initial Learning Phase for group 3, whereas groups 1 and 2 had no to minimal Learning time. The Learning Phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 ( p p p =0.22) or kidney transplant experience ( p =0.72). Conclusions The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. Patient summary Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption Phase.
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Application of the statistical process control method for prospective patient safety monitoring during the Learning Phase: robotic kidney transplantation with regional hypothermia (IDEAL Phase 2a-b).
European urology, 2014Co-Authors: Akshay Sood, Khurshid R Ghani, Rajesh Ahlawat, Pranjal Modi, Ronney Abaza, Wooju Jeong, Jesse D Sammon, Mireya Diaz, Vijay Kher, Mani MenonAbstract:Traditional evaluation of the Learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the Learning Phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial Learning Phase for group 3, whereas groups 1 and 2 had no to minimal Learning time. The Learning Phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft function was not adversely affected by either the lack of robotic training (p=0.22) or kidney transplant experience (p=0.72). The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption Phase. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Akshay Sood - One of the best experts on this subject based on the ideXlab platform.
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application of the statistical process control method for prospective patient safety monitoring during the Learning Phase robotic kidney transplantation with regional hypothermia ideal Phase 2a b
European Urology, 2014Co-Authors: Akshay Sood, Khurshid R Ghani, Rajesh Ahlawat, Pranjal Modi, Ronney Abaza, Wooju Jeong, Jesse D Sammon, Mireya Diaz, Vijay Kher, Mani MenonAbstract:Abstract Background Traditional evaluation of the Learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. Objectives To prospectively monitor patient safety during the Learning Phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). Design, setting and participants From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience ( n =7); group 2, robot trained and kidney transplant experienced ( n =20); and group 3, kidney transplant experienced with limited robot training ( n =14). Intervention We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcome measurements and statistical analysis Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. Results and limitations CUSUM analysis revealed an initial Learning Phase for group 3, whereas groups 1 and 2 had no to minimal Learning time. The Learning Phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 ( p p p =0.22) or kidney transplant experience ( p =0.72). Conclusions The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. Patient summary Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption Phase.
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Application of the statistical process control method for prospective patient safety monitoring during the Learning Phase: robotic kidney transplantation with regional hypothermia (IDEAL Phase 2a-b).
European urology, 2014Co-Authors: Akshay Sood, Khurshid R Ghani, Rajesh Ahlawat, Pranjal Modi, Ronney Abaza, Wooju Jeong, Jesse D Sammon, Mireya Diaz, Vijay Kher, Mani MenonAbstract:Traditional evaluation of the Learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the Learning Phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial Learning Phase for group 3, whereas groups 1 and 2 had no to minimal Learning time. The Learning Phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft function was not adversely affected by either the lack of robotic training (p=0.22) or kidney transplant experience (p=0.72). The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption Phase. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Diana Eastcott - One of the best experts on this subject based on the ideXlab platform.
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Computer-supported experiential Learning (Phase One - staff development)
Research in Learning Technology, 2011Co-Authors: Alan Staley, Diana EastcottAbstract:In recent years the University of Central England in Birmingham has made considerable investments in developing computer networks. Developments have been technology-led, and the major use of the network has been for administration. The Computer-Supported Experiential Learning Project has been designed to refocus upon the curriculum, and to encourage academic staff to use the network technologies for teaching and Learning. The broad aim of the project is to investigate and systematically evaluate the appropriate use of technology to improve the quality of Learning. DOI: 10.1080/0968776990070107
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Computer-supported experiential Learning (Phase One - staff development)
ALT-J, 1999Co-Authors: Alan Staley, Diana EastcottAbstract:The Computer‐Supported Experiential Learning Project has been established to promote the use of communication and information technologies for teaching and Learning within a vocational university. Phase 1 has concentrated upon raising awareness and actively involving academic staff in experiencing these technologies. The project is curriculum‐led, and considers how technology can be applied appropriately to an established curriculum model which links theory and practice (Kolb, 1984). All academic staff were invited to take part by logging onto the university intranet, accessing information about teaching and Learning, trying out ideas and emailing their online mentors with their plans and reflections. In addition, all staff could take part in discussion forums concerning a range of issues. The participation of academic staff is reported; which staff registered as having visited the site, which staff actively used the information to experiment with their teaching, and which staff took part in public online discussions. Barriers which limited participation are also reported The outcome of Phase 1 has been to encourage over 40 academic staff to embed the use of Learning technologies in their own course modules in Phase 2 with continued support from the Learning Methods Unit
Ana Inés Ansaldo - One of the best experts on this subject based on the ideXlab platform.
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Second Language Word Learning through Repetition and Imitation: Functional Networks as a Function of Learning Phase and Language Distance.
Frontiers in human neuroscience, 2017Co-Authors: Ladan Ghazi-saidi, Ana Inés AnsaldoAbstract:Introduction and Aim: Repetition and imitation are among the oldest second language (L2) teaching approaches and are frequently used in the context of L2 Learning and language therapy, despite some heavy criticism. Current neuroimaging techniques allow the neural mechanisms underlying repetition and imitation to be examined. This fMRI study examines the influence of verbal repetition and imitation on network configuration. Integration changes within and between the cognitive control and language networks were studied, in a pair of linguistically close languages (Spanish and French), and compared to our previous work on a distant language pair (Ghazi-Saidi et al., 2013). Methods: Twelve healthy native Spanish-speaking (L1) adults, and 12 healthy native Persian-speaking adults learned 130 new French (L2) words, through a computerized audiovisual repetition and imitation program. The program presented colored photos of objects. Participants were instructed to look at each photo and pronounce its name as closely as possible to the native template (imitate). Repetition was encouraged as many times as necessary to learn the object's name; phonological cues were provided if necessary. Participants practiced for 15 min, over 30 days, and were tested while naming the same items during fMRI scanning, at week 1 (shallow Learning Phase) and week 4 (consolidation Phase) of training. To compare this set of data with our previous work on Persian speakers, a similar data analysis plan including accuracy rates (AR), response times (RT), and functional integration values for the language and cognitive control network at each measure point was included, with further L1-L2 direct comparisons across the two populations. Results and Discussion: The evidence shows that Learning L2 words through repetition induces neuroplasticity at the network level. Specifically, L2 word learners showed increased network integration after 3 weeks of training, with both close and distant language pairs. Moreover, higher network integration was observed in the learners with the close language pair, suggesting that repetition effects on network configuration vary as a function of task complexity.
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Brain activation and lexical Learning: the impact of Learning Phase and word type.
NeuroImage, 2009Co-Authors: G. Raboyeau, Karine Marcotte, Daniel Adrover-roig, Ana Inés AnsaldoAbstract:Abstract This study investigated the neural correlates of second-language lexical acquisition in terms of Learning Phase and word type. Ten French-speaking participants learned 80 Spanish words–40 cognates, 40 non-cognates–by means of a computer program. The Learning process included the early Learning Phase, which comprised 5 days, and the consolidation Phase, which lasted 2 weeks. After each Phase, participants performed an overt naming task during an er-fMRI scan. Naming accuracy was better for cognates during the early Learning Phase only. However, cognates were named faster than non-cognates during both Phases. The early Learning Phase was characterized by activations in the left iFG and Broca's area, which were associated with effortful lexical retrieval and phonological processing, respectively. Further, the activation in the left ACC and DLPFC suggested that monitoring may be involved during the early Phases of lexical Learning. During the consolidation Phase, the activation in the left premotor cortex, the right supramarginal gyrus and the cerebellum indicated that articulatory planning may contribute to the consolidation of second-language phonetic representations. No dissociation between word type and Learning Phase could be supported. However, a Fisher r-to-z test showed that successful cognate retrieval was associated with activations in Broca's area, which could reflect the adaptation of known L1 phonological sequences. Moreover, successful retrieval of non-cognates was associated with activity in the anterior-medial left fusiform and right posterior cingulate cortices, suggesting that their successful retrieval may rely upon the access to semantic and lexical information, and even on the greater likelihood of errors.
Lionel Brunel - One of the best experts on this subject based on the ideXlab platform.
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Assessing the functional role of motor response during the integration process.
Journal of Experimental Psychology: Human Perception and Performance, 2016Co-Authors: Thomas Camus, Denis Brouillet, Lionel BrunelAbstract:The aim of this study was to provide evidence that actions performed by an individual influence the sensorimotor memory processing and, in particular, the integration process. We conducted 3 experiments that highlighted the multimodal aspect of memory traces. The 1st experiment consisted of a short-term priming paradigm based on 2 Phases: a Learning Phase, consisting of the association between a shape and a sound, and a test Phase, examining the priming effect of the shape seen in the Learning Phase on the processing of target tones. The participants’ motor response became a factor in Experiments 2 and 3, allowing us to observe its influence on the integration between the shape and the sound. In Experiment 1, we showed that (a) the prime associated with the sound in the Learning Phase had an effect on target processing and (b) the component reactivated by the prime was perceptual in nature (i.e., auditory). Experiment 2 showed that the participants’ responses were faster when the association of a shape and a sound had been learned with a motor response rather than without. Experiment 3 showed that the integration process required the individual to act while Learning the association between the shape and the sound; otherwise no integration effect was observed. Our results highlight the role of motor responses as a necessary criterion for the integration process to take place.
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When seeing a dog activates the bark multisensory generalization and distinctiveness effects
Experimental Psychology, 2013Co-Authors: Lionel Brunel, Benoit Riou, Guillaume Vallet, Robert L Goldstone, R??my VersaceAbstract:The goal of the present study was to find evidence for a multisensory generalization effect (i.e., generalization from one sensory modality to another sensory modality). The authors used an innovative paradigm (adapted from Brunel, Labeye, Lesourd, & Versace, 2009) involving three Phases: a Learning Phase, consisting in the categorization of geometrical shapes, which manipulated the rules of association between shapes and a sound feature, and two test Phases. The first of these was designed to examine the priming effect of the geometrical shapes seen in the Learning Phase on target tones (i.e., priming task), while the aim of the second was to examine the probability of recognizing the previously learned geometrical shapes (i.e., recognition task). When a shape category was mostly presented with a sound during Learning, all of the primes (including those not presented with a sound in the Learning Phase) enhanced target processing compared to a condition in which the primes were mostly seen without a sound during Learning. A pattern of results consistent with this initial finding was also observed during recognition, with the participants being unable to pick out the shape seen without a sound during the Learning Phase. Experiment 1 revealed a multisensory generalization effect across the members of a category when the objects belonging to the same category share the same value on the shape dimension. However, a distinctiveness effect was observed when a salient feature distinguished the objects within the category (Experiment 2a vs. 2b).
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When Seeing a Dog Activates the Bark
Experimental Psychology, 2013Co-Authors: Rémy Versace, Lionel Brunel, Guillaume Vallet, Robert L Goldstone, Benoit RiouAbstract:The goal of the present study was to find evidence for a multisensory generalization effect (i.e., generalization from one sensory modality to another sensory modality). The authors used an innovative paradigm (adapted from Brunel, Labeye, Lesourd, & Versace, 2009) involving three Phases: a Learning Phase, consisting in the categorization of geometrical shapes, which manipulated the rules of association between shapes and a sound feature, and two test Phases. The first of these was designed to examine the priming effect of the geometrical shapes seen in the Learning Phase on target tones (i.e., priming task), while the aim of the second was to examine the probability of recognizing the previously learned geometrical shapes (i.e., recognition task). When a shape category was mostly presented with a sound during Learning, all of the primes (including those not presented with a sound in the Learning Phase) enhanced target processing compared to a condition in which the primes were mostly seen without a sound during Learning. A pattern of results consistent with this initial finding was also observed during recognition, with the participants being unable to pick out the shape seen without a sound during the Learning Phase. Experiment 1 revealed a multisensory generalization effect across the members of a category when the objects belonging to the same category share the same value on the shape dimension. However, a distinctiveness effect was observed when a salient feature distinguished the objects within the category (Experiment 2a vs. 2b).
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The sensory nature of episodic memory: sensory priming effects due to memory trace activation.
Journal of experimental psychology. Learning memory and cognition, 2009Co-Authors: Lionel Brunel, Elodie Labeye, Mathieu Lesourd, Rémy VersaceAbstract:The aim of this study was to provide evidence that memory and perceptual processing are underpinned by the same mechanisms. Specifically, the authors conducted 3 experiments that emphasized the sensory aspect of memory traces. They examined their predictions with a short-term priming paradigm based on 2 distinct Phases: a Learning Phase consisting of the association between a geometrical shape and a white noise and a priming Phase examining the priming effect of the geometrical shape, seen in the Learning Phase, on the processing of target tones. In the 3 experiments, the authors found that only the prime associated with the sound in the Learning Phase had an effect on the target processing. The perceptual nature of the auditory component reactivated by the prime was shown in Experiments 1 and 2 via manipulation of the white noise duration in the Learning Phase and the stimulus onset asynchrony in the priming Phase. Moreover, Experiment 3 highlighted the importance of the simultaneous association of sensory components in the Learning Phase, which makes it possible to integrate these components in a memory trace.