899 resultados para Medical lab data


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Copyright © 2015 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved. Acknowledgements We would like to thank the Scottish Intensive Care Society Audit Group (SICSAG) for providing the data for this study. Mr Jan Jansen is in receipt of an NHS Research Scotland fellowship which includes salary funding.

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Funded by Chief Scientist Office, Scotland. Grant Number: CZH/4/394 Economic and Social Research Council grant as part of the National Centre for Research Methods. Grant Number: RES-576-25-0032

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Conflict of interest: None of the authors have any conflict of interest.

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© The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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Acknowledgements We thank Brian Roberts and Mike Harris for responding to our questions regarding their paper; Zoltan Dienes for advice on Bayes factors; Denise Fischer, Melanie Römer, Ioana Stanciu, Aleksandra Romanczuk, Stefano Uccelli, Nuria Martos Sánchez, and Rosa María Beño Ruiz de la Sierra for help collecting data; Eva Viviani for managing data collection in Parma. We thank Maurizio Gentilucci for letting us use his lab, and the Centro Intradipartimentale Mente e Cervello (CIMeC), University of Trento, and especially Francesco Pavani for lending us his motion tracking equipment. We thank Rachel Foster for proofreading. KKK was supported by a Ph.D. scholarship as part of a grant to VHF within the International Graduate Research Training Group on Cross-Modal Interaction in Natural and Artificial Cognitive Systems (CINACS; DFG IKG-1247) and TS by a grant (DFG – SCHE 735/3-1); both from the German Research Council.

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The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.

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Postprint

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Funding and trial registration: Scottish Government Chief Scientist Office grant CZH/3/17. ClinicalTrials.gov registration NCT01602705.

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Funding Sources The NNUH Stroke and TIA Register is maintained by the NNUH NHS Foundation Trust Stroke Services and data management for this study is supported by the NNUH Research and Development Department through Research Capability Funds.

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Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this dissertation, two different approaches to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, were studied and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a first approach, the use of geometric transformations to perform inter-slice prediction was investigated. For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction, that exploits inter-slice redundancy was proposed to extend the current HEVC lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when compared with DICOM recommended encoders, and 13.7% when compared with standard HEVC.

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Background: Heavy menstrual bleeding (HMB) is a common, chronic problem affecting women and health services. However, long-term evidence on treatment in primary care is lacking. Aim: To assess the effectiveness of commencing the levonorgestrel-releasing intrauterine system (LNG-IUS) or usual medical treatments for women presenting with HMB in general practice. Design and setting: A pragmatic, multicentre, parallel, open-label, long term, randomised controlled trial in 63 primary care practices across the English Midlands. Method: In total, 571 women aged 25–50 years, with HMB were randomised to LNG-IUS or usual medical treatment (tranexamic/mefenamic acid, combined oestrogen–progestogen, or progesterone alone). The primary outcome was the patient reported Menorrhagia Multi-Attribute Scale (MMAS, measuring effect of HMB on practical difficulties, social life, psychological and physical health, and work and family life; scores from 0 to 100). Secondary outcomes included surgical intervention (endometrial ablation/hysterectomy), general quality of life, sexual activity, and safety. Results: At 5 years post-randomisation, 424 (74%) women provided data. While the difference between LNG-IUS and usual treatment groups was not significant (3.9 points; 95% confidence interval = −0.6 to 8.3; P = 0.09), MMAS scores improved significantly in both groups from baseline (mean increase, 44.9 and 43.4 points, respectively; P<0.001 for both comparisons). Rates of surgical intervention were low in both groups (surgery-free survival was 80% and 77%; hazard ratio 0.90; 95% CI = 0.62 to 1.31; P = 0.6). There was no difference in generic quality of life, sexual activity scores, or serious adverse events. Conclusion: Large improvements in symptom relief across both groups show treatment for HMB can be successfully initiated with long-term benefit and with only modest need for surgery.

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Head motion during a Positron Emission Tomography (PET) brain scan can considerably degrade image quality. External motion-tracking devices have proven successful in minimizing this effect, but the associated time, maintenance, and workflow changes inhibit their widespread clinical use. List-mode PET acquisition allows for the retroactive analysis of coincidence events on any time scale throughout a scan, and therefore potentially offers a data-driven motion detection and characterization technique. An algorithm was developed to parse list-mode data, divide the full acquisition into short scan intervals, and calculate the line-of-response (LOR) midpoint average for each interval. These LOR midpoint averages, known as “radioactivity centroids,” were presumed to represent the center of the radioactivity distribution in the scanner, and it was thought that changes in this metric over time would correspond to intra-scan motion.

Several scans were taken of the 3D Hoffman brain phantom on a GE Discovery IQ PET/CT scanner to test the ability of the radioactivity to indicate intra-scan motion. Each scan incrementally surveyed motion in a different degree of freedom (2 translational and 2 rotational). The radioactivity centroids calculated from these scans correlated linearly to phantom positions/orientations. Centroid measurements over 1-second intervals performed on scans with ~1mCi of activity in the center of the field of view had standard deviations of 0.026 cm in the x- and y-dimensions and 0.020 cm in the z-dimension, which demonstrates high precision and repeatability in this metric. Radioactivity centroids are thus shown to successfully represent discrete motions on the submillimeter scale. It is also shown that while the radioactivity centroid can precisely indicate the amount of motion during an acquisition, it fails to distinguish what type of motion occurred.