Bleeding changes after levonorgestrel 52-mg intrauterine system insertion for contraception in women with self-reported heavy menstrual bleeding.

BackgroundThe levonorgestrel 52-mg intrauterine system has proven efficacy for heavy menstrual bleeding treatment in clinical trials, but few data exist to demonstrate how rapidly the effects occur and the effects in women with self-reported heavy bleeding, as seen commonly in clinical practice.ObjectiveEvaluate changes in bleeding patterns in women with self-reported heavy menstrual bleeding before levonorgestrel 52-mg intrauterine system insertion.Study designA total of 1714 women aged 16-45 years old received a levonorgestrel 52-mg intrauterine system in a multicenter trial evaluating contraceptive efficacy and safety for up to 10 years. At screening, participants described their baseline menstrual bleeding patterns for the previous 3 months. Participants completed daily diaries with subjective evaluation of bleeding information for the first 2 years. For this analysis, we included women with at least 1 complete 28-day cycle of intrauterine system use and excluded women using a hormonal or copper intrauterine contraception in the month prior to study enrollment. We evaluated changes in menstrual bleeding and discontinuation for bleeding complaints per 28-day cycle over 26 cycles (2 years) in women who self-reported their baseline pattern as heavy. We also compared rates of amenorrhea, defined as no bleeding or spotting, within the entire study population in women with subjective heavy menstrual bleeding at baseline compared with those who did not complain of heavy menstrual bleeding.ResultsOf the 1513 women in this analysis, 150 (9.9%) reported baseline heavy menstrual bleeding. The majority of women reported no longer experiencing heavy menstrual bleeding by the end of cycle 1 (112/150, 74.7%) with even greater rates by cycle 2 (124/148, 83.8%). At the end of cycles 6, 13, and 26, 129 of 140 (92.1%; 95% confidence interval, 87.7%-96.6%), 114 of 123 (92.7%; 95% confidence interval, 88.1%-97.3%), and 100 of 103 (97.1%; 95% confidence interval, 93.8%-100%) women reported no heavy menstrual bleeding, respectively. After cycles 13 and 26, 63 of 123 (51.2%; 95% confidence interval, 42.4%-60.1%) and 66 of 103 (64.1%; 95% confidence interval, 54.8%-73.3%), respectively, reported their bleeding as amenorrhea or spotting only. A lower proportion of women with baseline self-reported heavy menstrual bleeding reported amenorrhea as compared with women in the overall study cohort without heavy menstrual bleeding at the end of 6 cycles (319 [25.5%] vs 21 [15.0%], P=.005) and 13 cycles (382 [34.4%] vs 26 [21.1%], P=.003); differences were not significant after 19 cycles (367 [37.2%] vs 36 [31.0%], P=.022) and 26 cycles (383 [43.5%] vs 38 [36.9%], P=.21). Only 4 (2.7%) women with baseline heavy menstrual bleeding discontinued for bleeding complaints (2 for heavy menstrual bleeding and 2 for irregular bleeding), all within the first year.ConclusionMost women who self-report heavy menstrual bleeding experience significant improvement quickly after levonorgestrel 52-mg intrauterine system insertion. Discontinuation for bleeding complaints among women with baseline heavy menstrual bleeding is very low

Preserving Beijing's Old City : the vision and reality of historic conservation planning

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2003.Includes bibliographical references (leaves 68-72).In 2000, the Beijing Municipal Planning Commission drew up a conservation plan for the 25 historic areas in Beijing's Old City. The main principles for the conservation plan were as follows: 1) To preserve the traditional cityscape and hutongs, 2) To ensure the authenticity of the preserved heritage, 3) To implement preservation using a gradual and measured method, 4) To improve the infrastructure and living conditions of the local residents, and 5) To encourage public participation. The residential district of Nanchizi was one of the 25 designated historic areas. In 2001, the area became the pilot site for the implementation of the conservation plan. In June 2002, demolition crews arrived at Nanchizi ready to destroy the courtyard homes. The resulting transformation of Nanchizi reveals that the plan did not succeed in preserving the historic area. This thesis explains why this initial implementation of the historic conservation plan failed to achieve the principles of the plan and examines the causes of this disparity between the vision and the reality of conservation planning in Beijing. It argues that the cause of the disparity between vision and reality lies in the competing visions of those involved in the planning process and that certain visions are valued over others. This cause stems from the inability of planning institutions and processes to respond to the political and economic transitions of China in the past decade. The thesis concludes that in order to succeed in conservation, the function and process of planning must be reconsidered. In addition, the discourse on conservation should also be reexamined in order to ensure that other designated conservation districts of Beijing do not suffer the same fate as the Nanchizi historic area.by Beatrice B. Chen.M.C.P

Influence of shear waves on transcranial ultrasound propagation in cortical brain regions

Transcranial ultrasound applications require accurate simulations to predict intracranial acoustic pressure fields. The current gold standard typically consists of calculating a longitudinal ultrasound wave propagation using a fluid skull model, which is based on full head CT images for retrieving the skull's geometry and elastic constants. Although this approach has extensively been validated for deep brain targets and routinely used in transcranial ultrasound ablation procedures, its accuracy in shallow cortical regions remains unexplored. In this study, we explore the shear wave effects associated with transcranial focused ultrasound propagation, both numerically and experimentally. The intracranial acoustic pressure was measured at different incidence angles at the parietal and frontal regions in an ex vivo human skull. The fluid-like skull model was then compared to the solid model comprising both longitudinal and shear waves. The results consistently show a larger error and variability for both models when considering an oblique incidence, reaching a maximum of 125% mean deviation of the focal area when employing the fluid skull model. Statistical assessments further revealed that ignoring shear waves results in an average ~40% overestimation of the intracranial acoustic pressure and inability to obtain an accurate intracranial acoustic pressure distribution. Moreover, the solid model has a more stable performance, even when small variations in the skull-transducer relative position are introduced. Our results could contribute to the refinement of the transcranial ultrasound propagation modeling methods thus help improving the safety and outcome of transcranial ultrasound therapy in the cortical brain areas

The Effect of Intergroup Contact in Gaming on Improving Empathetic Feelings and Reducing Stereotypes Toward Immigrants

Video games have been designed and studied in the context of intergroup relations. Past studies have shown that overall gaming experience may induce empathy and reduce prejudice, however, the mechanism that leads to such an effect is not fully understood. The current study utilizes intergroup contact theory as a foundation to design a 2D game that allows mediated contact to occur through hiring and dialogue choices. It examines how perceived positive vs. negative contact valence with outgroup immigrant workers influences empathetic feelings and stereotypes toward immigrants. A user study of 81 adult players was conducted. Results showed that overall gameplay reduced negative stereotypes. Perceived positive contact predicts increased empathetic feelings and reduced negative stereotypes toward immigrants. However, there is no significant finding with regards to the influence of negative contact in the game. Implications and directions for further research are discusse

Automated Clinical Coding:What, Why, and Where We Are?

Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019 - early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.Comment: accepted for npj Digital Medicin

Automated clinical coding:What, why, and where we are?

Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html . Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. 1 , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html. Publisher Copyright: © 2022, The Author(s).Clinical coding is the task of transforming medical information in a patient’s health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019–early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable processof a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.Peer reviewe