866 resultados para Predictors


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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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Acknowledgment MN's PhD scholarship was provided by Ministry of Health and Medical Education (Islamic Republic of Iran). This study was funded by the University of Aberdeen. FFS is funded by Fuse, the UK Clinical Research Collaboration Centre of Excellence for Translational Research in Public Health. The researchers gratefully acknowledge all the Type 2 diabetic patients and their household members who participated in the study for their contribution to this study; without them there would be no data. The researchers gratefully acknowledge the SDRN for providing the list of Type 2 diabetes and helping for sampling.

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Financial disclosures/conflicts of interest: Dr Macleod was funded by a Clinical Academic Fellowship from the Chief Scientist Office of the Scottish Government and received grant funding from Parkinson’s UK, the Wellcome Trust, University of Aberdeen, and NHS Grampian endowments relating to this research. Dr Counsell received grant funding from Parkinson’s UK, National Institute for Health Research, the Scottish Chief Scientist Office, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, NHS Grampian endowments and SPRING relating to this research. We declare we have no conflicts of interest. Financial support: This study was funded by Parkinson’s UK, the Scottish Chief Scientist Office, NHS Grampian endowments, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, and SPRING.  

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Financial disclosures/conflicts of interest: Dr Macleod was funded by a Clinical Academic Fellowship from the Chief Scientist Office of the Scottish Government and received grant funding from Parkinson’s UK, the Wellcome Trust, University of Aberdeen, and NHS Grampian endowments relating to this research. Dr Counsell received grant funding from Parkinson’s UK, National Institute for Health Research, the Scottish Chief Scientist Office, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, NHS Grampian endowments and SPRING relating to this research. We declare we have no conflicts of interest. Financial support: This study was funded by Parkinson’s UK, the Scottish Chief Scientist Office, NHS Grampian endowments, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, and SPRING.  

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Acknowledgment MN's PhD scholarship was provided by Ministry of Health and Medical Education (Islamic Republic of Iran). This study was funded by the University of Aberdeen. FFS is funded by Fuse, the UK Clinical Research Collaboration Centre of Excellence for Translational Research in Public Health. The researchers gratefully acknowledge all the Type 2 diabetic patients and their household members who participated in the study for their contribution to this study; without them there would be no data. The researchers gratefully acknowledge the SDRN for providing the list of Type 2 diabetes and helping for sampling.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

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Medical imaging technologies are experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like CT or MRI. Furthermore, legal restrictions impose that these scans must be archived for several years. These facts led to the increase of storage costs in medical image databases and institutions. Thus, a demand for more efficient compression tools, used for archiving and communication, is arising. Currently, the DICOM standard, that makes recommendations for medical communications and imaging compression, recommends lossless encoders such as JPEG, RLE, JPEG-LS and JPEG2000. However, none of these encoders include inter-slice prediction in their algorithms. This dissertation presents the research work on medical image compression, using the MRP encoder. MRP is one of the most efficient lossless image compression algorithm. Several processing techniques are proposed to adapt the input medical images to the encoder characteristics. Two of these techniques, namely changing the alignment of slices for compression and a pixel-wise difference predictor, increased the compression efficiency of MRP, by up to 27.9%. Inter-slice prediction support was also added to MRP, using uni and bi-directional techniques. Also, the pixel-wise difference predictor was added to the algorithm. Overall, the compression efficiency of MRP was improved by 46.1%. Thus, these techniques allow for compression ratio savings of 57.1%, compared to DICOM encoders, and 33.2%, compared to HEVC RExt Random Access. This makes MRP the most efficient of the encoders under study.

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Background:

Knowing the scope of neurosurgical disease at Mbarara Hospital is critical for infrastructure planning, education and training. In this study, we aim to evaluate the neurosurgical outcomes and identify predictors of mortality in order to potentiate platforms for more effective interventions and inform future research efforts at Mbarara Hospital.

Methods:

This is retrospective chart review including patients of all ages with a neurosurgical disease or injury presenting to Mbarara Regional Referral Hospital (MRRH) between January 2012 to September 2015. Descriptive statistics were presented. A univariate analysis was used to obtain the odds ratios of mortality and 95% confidence intervals. Predictors of mortality were determined using multivariate logistic regression model.

Results:

A total of 1876 charts were reviewed. Of these, 1854 (had complete data and were?) were included in the analysis. The overall mortality rate was 12.75%; the mortality rates among all persons who underwent a neurosurgical procedure was 9.72%, and was 13.68% among those who did not undergo a neurosurgical procedure. Over 50% of patients were between 19 and 40 years old and the majority of were males (76.10%). The overall median length of stay was 5 days. Of all neurosurgical admissions, 87% were trauma patients. In comparison to mild head injury, closed head injury and intracranial hematoma patients were 5 (95% CI: 3.77, 8.26) and 2.5 times (95% CI: 1.64,3.98) more likely to die respectively. Procedure and diagnostic imaging were independent negative predictors of mortality (P <0.05). While age, ICU admission, admission GCS were positive predictors of mortality (P <0.05).

Conclusions:

The majority of hospital admissions were TBI patients, with RTIs being the most common mechanism of injury. Age, ICU admission, admission GCS, diagnostic imaging and undergoing surgery were independent predictors of mortality. Going forward, further exploration of patient characteristics is necessary to fully describe mortality outcomes and implement resource appropriate interventions that ultimately improve morbidity and mortality.

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Background Delirium is highly prevalent, especially in older patients. It independently leads to adverse outcomes, but remains under-detected, particularly hypoactive forms. Although early identification and intervention is important, delirium prevention is key to improving outcomes. The delirium prodrome concept has been mooted for decades, but remains poorly characterised. Greater understanding of this prodrome would promote prompt identification of delirium-prone patients, and facilitate improved strategies for delirium prevention and management. Methods Medical inpatients of ≥70 years were screened for prevalent delirium using the Revised Delirium Rating Scale (DRS--‐R98). Those without prevalent delirium were assessed daily for delirium development, prodromal features and motor subtype. Survival analysis models identified which prodromal features predicted the emergence of incident delirium in the cohort in the first week of admission. The Delirium Motor Subtype Scale-4 was used to ascertain motor subtype. Results Of 555 patients approached, 191 patients were included in the prospective study. The median age was 80 (IQR 10) and 101 (52.9%) were male. Sixty-one patients developed incident delirium within a week of admission. Several prodromal features predicted delirium emergence in the cohort. Firstly, using a novel Prodromal Checklist based on the existing literature, and controlling for confounders, seven predictive behavioural features were identified in the prodromal period (for example, increasing confusion; and being easily distractible). Additionally, using serial cognitive tests and the DRS-R98 daily, multiple cognitive and other core delirium features were detected in the prodrome (for example inattention; and sleep-wake cycle disturbance). Examining longitudinal motor subtypes in delirium cases, subtypes were found to be predominantly stable over time, the most prevalent being hypoactive subtype (62.3%). Discussion This thesis explored multiple aspects of delirium in older medical inpatients, with particular focus on the characterisation of the delirium prodrome. These findings should help to inform future delirium educational programmes, and detection and prevention strategies.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

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INTRODUCTION Zero-G parabolic flight reproduces the weightlessness of space for short periods of time. However motion sickness may affect some fliers. The aim was to assess the extent of this problem and to find possible predictors and modifying factors. METHODS Airbus Zero-G flights consist of 31 parabolas performed in blocks. Each parabola consisted of 20s 0g sandwiched by 20s hypergravity of 1.5-1.8g. The survey covered n=246 person-flights (193 Males 53 Females), aged (M+/-SD) 36.0+/-11.3 years. An anonymous questionnaire included motion sickness rating (1=OK to 6=Vomiting), Motion Sickness Susceptibility Questionnaire (MSSQ), anti-motion sickness medication, prior Zero-G experience, anxiety level, and other characteristics. RESULTS Participants had lower MSSQ percentile scores 27.4+/-28.0 than the population norm of 50. Motion sickness was experienced by 33% and 12% vomited. Less motion sickness was predicted by older age, greater prior Zero-G flight experience, medication with scopolamine, lower MSSQ scores, but not gender nor anxiety. Sickness ratings in fliers pre-treated with scopolamine (1.81+/-1.58) were lower than for non-medicated fliers (2.93+/-2.16), and incidence of vomiting in fliers using scopolamine treatment was reduced by half to a third. Possible confounding factors including age, sex, flight experience, MSSQ, could not account for this. CONCLUSION Motion sickness affected one third of Zero-G fliers, despite being intrinsically less motion sickness susceptible compared to the general population. Susceptible individuals probably try to avoid such a provocative environment. Risk factors for motion sickness included younger age and higher MSSQ scores. Protective factors included prior Zero-G flight experience (habituation) and anti-motion sickness medication.