\n
\n\n \n \n \n \n \n Use of the theoretical domains framework and behaviour change wheel to develop a novel intervention to improve the quality of multidisciplinary cancer conference decision-making.\n \n \n \n\n\n \n Fahim, C.; Fahim, C.; Acai, A.; McConnell, M. M.; Wright, F. C.; Sonnadara, R. R.; Simunovic, M.; and Simunovic, M.\n\n\n \n\n\n\n
BMC Health Services Research, 20(1): 1–19. 2020.\n
\n\n
\n\n
\n\n
\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{Fahim2020a,\nabstract = {Background: Multidisciplinary Cancer Conferences (MCCs) are prospective meetings involving cancer specialists to discuss treatment plans for patients with cancer. Despite reported gaps in MCC quality, there have been few efforts to improve its functioning. The purpose of this study was to use theoretically-rooted knowledge translation (KT) theories and frameworks to inform the development of a strategy to improve MCC decision-making quality. Methods: A multi-phased approach was used to design an intervention titled the KT-MCC Strategy. First, key informant interviews framed using the Theoretical Domains Framework (TDF) were conducted with MCC participants to identify barriers and facilitators to optimal MCC decision-making. Second, identified TDF domains were mapped to corresponding strategies using the COM-B Behavior Change Wheel to develop the KT-MCC Strategy. Finally, focus groups with MCC participants were held to confirm acceptability of the proposed KT-MCC Strategy. Results: Data saturation was reached at n = 21 interviews. Twenty-seven barrier themes and 13 facilitator themes were ascribed to 11 and 10 TDF domains, respectively. Differences in reported barriers by physician specialty were observed. The resulting KT-MCC Strategy included workshops, chair training, team training, standardized intake forms and a synoptic discussion checklist, and, audit and feedback. Focus groups (n = 3, participants 18) confirmed the acceptability of the identified interventions. Conclusion: Myriad factors were found to influence MCC decision making. We present a novel application of the TDF and COM-B to the context of MCCs. We comprehensively describe the barriers and facilitators that impact MCC decision making and propose strategies that may positively impact the quality of MCC decision making.},\nauthor = {Fahim, Christine and Fahim, Christine and Acai, Anita and McConnell, Meghan M. and Wright, Frances C. and Sonnadara, Ranil R. and Simunovic, Marko and Simunovic, Marko},\ndoi = {10.1186/s12913-020-05255-w},\nfile = {:C$\\backslash$:/Users/cindy/Downloads/Use{\\_}of{\\_}the{\\_}theoretical{\\_}domains.pdf:pdf},\nissn = {14726963},\njournal = {BMC Health Services Research},\nkeywords = {COM-B behaviour change wheel,Cancer,Intervention design,Knowledge translation,Multidisciplinary cancer conference,Multidisciplinary decision making,Multidisciplinary tumor board,Qualitative research,Theoretical domains framework},\nnumber = {1},\npages = {1--19},\npmid = {32580767},\npublisher = {BMC Health Services Research},\ntitle = {{Use of the theoretical domains framework and behaviour change wheel to develop a novel intervention to improve the quality of multidisciplinary cancer conference decision-making}},\nvolume = {20},\nyear = {2020}\n}\n
\n
\n\n\n
\n Background: Multidisciplinary Cancer Conferences (MCCs) are prospective meetings involving cancer specialists to discuss treatment plans for patients with cancer. Despite reported gaps in MCC quality, there have been few efforts to improve its functioning. The purpose of this study was to use theoretically-rooted knowledge translation (KT) theories and frameworks to inform the development of a strategy to improve MCC decision-making quality. Methods: A multi-phased approach was used to design an intervention titled the KT-MCC Strategy. First, key informant interviews framed using the Theoretical Domains Framework (TDF) were conducted with MCC participants to identify barriers and facilitators to optimal MCC decision-making. Second, identified TDF domains were mapped to corresponding strategies using the COM-B Behavior Change Wheel to develop the KT-MCC Strategy. Finally, focus groups with MCC participants were held to confirm acceptability of the proposed KT-MCC Strategy. Results: Data saturation was reached at n = 21 interviews. Twenty-seven barrier themes and 13 facilitator themes were ascribed to 11 and 10 TDF domains, respectively. Differences in reported barriers by physician specialty were observed. The resulting KT-MCC Strategy included workshops, chair training, team training, standardized intake forms and a synoptic discussion checklist, and, audit and feedback. Focus groups (n = 3, participants 18) confirmed the acceptability of the identified interventions. Conclusion: Myriad factors were found to influence MCC decision making. We present a novel application of the TDF and COM-B to the context of MCCs. We comprehensively describe the barriers and facilitators that impact MCC decision making and propose strategies that may positively impact the quality of MCC decision making.\n
\n\n\n
\n
\n\n \n \n \n \n \n On the time-course of functional connectivity: theory of a dynamic progression of concussion effects.\n \n \n \n\n\n \n Boshra, R.; Ruiter, K. I; Dhindsa, K.; Sonnadara, R.; Reilly, J. P; and Connolly, J. F\n\n\n \n\n\n\n
Brain Communications, 2(2): 1–14. 2020.\n
\n\n
\n\n
\n\n
\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{Boshra2020,\nabstract = {The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.},\nauthor = {Boshra, Rober and Ruiter, Kyle I and Dhindsa, Kiret and Sonnadara, Ranil and Reilly, James P and Connolly, John F},\ndoi = {10.1093/braincomms/fcaa063},\nfile = {:C$\\backslash$:/Users/cindy/Downloads/fcaa063.pdf:pdf},\njournal = {Brain Communications},\nkeywords = {19,2019,2020,abbreviations,accepted april 24,advance access publication may,brain injury,eeg ¼ electroencephalography,electroencephalography,erp ¼ event-related potential,fc ¼ functional connectivity,functional connectivity,index,mtbi ¼ mild,progression,received november 18,revised april 15,theoretical model,traumatic brain injury,wpli ¼ weighted phase-lag},\nnumber = {2},\npages = {1--14},\ntitle = {{On the time-course of functional connectivity: theory of a dynamic progression of concussion effects}},\nvolume = {2},\nyear = {2020}\n}\n
\n
\n\n\n
\n The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.\n
\n\n\n
\n
\n\n \n \n \n \n \n \n Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct .\n \n \n \n \n\n\n \n Smail, L. C; Dhindsa, K.; Braga, L. H; Becker, S.; and Sonnadara, R. R\n\n\n \n\n\n\n 2020.\n
\n\n
\n\n
\n\n
\n\n \n \n Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@misc{Smail2020,\nabstract = {Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2,420 sagittal hydronephrosis ultrasound images [191 SFU 0 (8{\\%}), 407 SFU I (17{\\%}), 666 SFU II (28{\\%}), 833 SFU III (34{\\%}), and 323 SFU IV (13{\\%})], from 673 patients ranging from 0 to 116.29 months old (M{\\textless}sub{\\textgreater}age{\\textless}/sub{\\textgreater} = 16.53, SD = 17.80). Five-way (all grades) and two-way classification problems [i.e., II vs. III, and low (0–II) vs. high (III–IV)] were explored. The CNN classified 94{\\%} (95{\\%} CI, 93–95{\\%}) of the images correctly or within one grade of the provided label in the five-way classification problem. Fifty-one percent of these images (95{\\%} CI, 49–53{\\%}) were correctly predicted, with an average weighted F1 score of 0.49 (95{\\%} CI, 0.47–0.51). The CNN achieved an average accuracy of 78{\\%} (95{\\%} CI, 75–82{\\%}) with an average weighted F1 of 0.78 (95{\\%} CI, 0.74–0.82) when classifying low vs. high grades, and an average accuracy of 71{\\%} (95{\\%} CI, 68–74{\\%}) with an average weighted F1 score of 0.71 (95{\\%} CI, 0.68–0.75) when discriminating between grades II vs. III. Our model performs well above chance level, and classifies almost all images either correctly or within one grade of the provided label. We have demonstrated the applicability of a CNN approach to hydronephrosis ultrasound image classification. Further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.},\nauthor = {Smail, Lauren C and Dhindsa, Kiret and Braga, Luis H and Becker, Suzanna and Sonnadara, Ranil R},\nbooktitle = {Frontiers in Pediatrics },\nisbn = {2296-2360},\npages = {1},\ntitle = {{Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct }},\nurl = {https://www.frontiersin.org/article/10.3389/fped.2020.00001},\nvolume = {8 },\nyear = {2020}\n}\n
\n
\n\n\n
\n Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2,420 sagittal hydronephrosis ultrasound images [191 SFU 0 (8%), 407 SFU I (17%), 666 SFU II (28%), 833 SFU III (34%), and 323 SFU IV (13%)], from 673 patients ranging from 0 to 116.29 months old (M\\textlesssub\\textgreaterage\\textless/sub\\textgreater = 16.53, SD = 17.80). Five-way (all grades) and two-way classification problems [i.e., II vs. III, and low (0–II) vs. high (III–IV)] were explored. The CNN classified 94% (95% CI, 93–95%) of the images correctly or within one grade of the provided label in the five-way classification problem. Fifty-one percent of these images (95% CI, 49–53%) were correctly predicted, with an average weighted F1 score of 0.49 (95% CI, 0.47–0.51). The CNN achieved an average accuracy of 78% (95% CI, 75–82%) with an average weighted F1 of 0.78 (95% CI, 0.74–0.82) when classifying low vs. high grades, and an average accuracy of 71% (95% CI, 68–74%) with an average weighted F1 score of 0.71 (95% CI, 0.68–0.75) when discriminating between grades II vs. III. Our model performs well above chance level, and classifies almost all images either correctly or within one grade of the provided label. We have demonstrated the applicability of a CNN approach to hydronephrosis ultrasound image classification. Further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.\n
\n\n\n