833 resultados para dissimilarity measures
Resumo:
PURPOSE: Comparing the relative effectiveness of interventions across glaucoma trials can be problematic due to differences in definitions of outcomes. We sought to identify a key set of clinical outcomes and reach consensus on how best to measure them from the perspective of glaucoma experts.
METHODS: A 2-round electronic Delphi survey was conducted. Round 1 involved 25 items identified from a systematic review. Round 2 was developed based on information gathered in round 1. A 10-point Likert scale was used to quantify importance and consensus of outcomes (7 outcomes) and ways of measuring them (44 measures). Experts were identified through 2 glaucoma societies membership-the UK and Eire Glaucoma Society and the European Glaucoma Society. A Nominal Group Technique (NGT) followed the Delphi process. Results were analyzed using descriptive statistics.
RESULTS: A total of 65 participants completed round 1 out of 320; of whom 56 completed round 2 (86%). Agreement on the importance of outcomes was reached on 48/51 items (94%). Intraocular pressure (IOP), visual field (VF), safety, and anatomic outcomes were classified as highly important. Regarding methods of measurement of IOP, "mean follow-up IOP" using Goldmann applanation tonometry achieved the highest importance, whereas for evaluating VFs "global index mean deviation/defect (MD)" and "rate of VF progression" were the most important. Retinal nerve fiber layer (RNFL) thickness measured by optical coherence tomography (OCT) was identified as highly important. The NGT results reached consensus on "change of IOP (mean of 3 consecutive measurements taken at fixed time of day) from baseline," change of VF-MD values (3 reliable VFs at baseline and follow-up visit) from baseline, and change of RNFL thickness (2 good quality OCT images) from baseline.
CONCLUSIONS: Consensus was reached among glaucoma experts on how best to measure IOP, VF, and anatomic outcomes in glaucoma randomized controlled trials.
Resumo:
OBJECTIVE: Obesity in the offspring of women with hyperglycemia during pregnancy has been reported, but the results are conflicting. This study examined the association of hyperglycemia during pregnancy and anthropometry in 5- to 7-year-old offspring whose mothers participated in the Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) Study at the Belfast Centre.
RESEARCH DESIGN AND METHODS: Women in the HAPO study underwent a 75-g oral glucose tolerance test (OGTT) at approximately 28 weeks of gestation. Mothers and caregivers remained blinded to the results unless the fasting plasma glucose (FPG) concentration was >5.8 mmol/L or the 2-h plasma glucose (2hPG) concentration was >11.1 mmol/L. Offspring weight, height, and skin-fold thicknesses (triceps, subscapular, and suprailiac) were measured at age 5-7 years. Overweight, obesity, and extreme obesity were defined as a BMI z score ≥85th, ≥95th, and ≥99th percentile, respectively, based on the 1990 British Growth Standard.
RESULTS: Belfast HAPO offspring (n = 1,320, 82%) aged 5-7 years attended for follow-up. Using multiple regression, maternal FPG, 1h PG, and 2hPG did not show any relation to offspring BMI z score or offspring skin-fold sum independent of maternal BMI at OGTT and offspring birth weight z score. This lack of association with maternal glycemia persisted with the offspring BMI z score expressed as ≥85th, ≥95th, or 99th percentile, and the sum of skin folds expressed as ≥90th percentile specific for sex. The initially significant relation between FPG and all offspring adiposity measures was explained by maternal BMI at the OGTT.
CONCLUSIONS: After adjustment for maternal BMI at the OGTT, higher maternal FPG concentration during pregnancy (short of diabetes) is no longer a risk factor for obesity, as reflected by BMI and the sum of skin folds in offspring aged 5-7 years.
Resumo:
This chapter explores the use of community sanctions in the Republic of Ireland and in Northern Ireland. It locates this discussion within a wider international landscape, where the numbers of people subject to supervision in the community has risen markedly. It explores some of the reasons for this growth alongside the rationalities that are deployed to promote the use of community sanctions over time. The differing trajectories of the two jurisdictions in respect of the evolution and use of community sanctions are explored, as are some of the factors that explain areas of divergence and commonality. The chapter concludes by critically considering penal reductionism as a point of policy convergence in the two jurisdictions.
Resumo:
A RkNN query returns all objects whose nearest k neighbors
contain the query object. In this paper, we consider RkNN
query processing in the case where the distances between
attribute values are not necessarily metric. Dissimilarities
between objects could then be a monotonic aggregate of dissimilarities
between their values, such aggregation functions
being specified at query time. We outline real world cases
that motivate RkNN processing in such scenarios. We consider
the AL-Tree index and its applicability in RkNN query
processing. We develop an approach that exploits the group
level reasoning enabled by the AL-Tree in RkNN processing.
We evaluate our approach against a Naive approach
that performs sequential scans on contiguous data and an
improved block-based approach that we provide. We use
real-world datasets and synthetic data with varying characteristics
for our experiments. This extensive empirical
evaluation shows that our approach is better than existing
methods in terms of computational and disk access costs,
leading to significantly better response times.
Resumo:
Background
Neighbourhood segregation has been described as a fundamental determinant of physical health, but literature on its effect on mental health is less clear. Whilst most previous research has relied on conceptualized measures of segregation, Northern Ireland is unique as it contains physical manifestations of segregation in the form of segregation barriers (or “peacelines”) which can be used to accurately identify residential segregation.
Methods
We used population-wide health record data on over 1.3 million individuals, to analyse the effect of residential segregation, measured by both the formal Dissimilarity Index and by proximity to a segregation barrier, on the likelihood of poor mental health.
Results
Using multi-level logistic regression models we found residential segregation measured by the Dissimilarity Index poses no additional risk to the likelihood of poor mental health after adjustment for area-level deprivation. However, residence in an area segregated by a “peaceline” increases the likelihood of antidepressant medication by 19% (OR=1.19, 95% CI: 1.14, 1.23) and anxiolytic medication by 39% (OR=1.39, 95% CI: 1.32, 1.48), even after adjustment for gender, age, conurbation, deprivation and crime.
Conclusions
Living in an area segregated by a ‘peaceline’ is detrimental to mental health suggesting segregated areas characterised by a heightened sense of ‘other’ pose a greater risk to mental health. The difference in results based on segregation measure highlights the importance of choice of measure when studying segregation.
Resumo:
We describe five children who died of clinical rabies in a three month period (September to November 2011) in the Queen Elizabeth Central Hospital. From previous experience and hospital records, this number of cases is higher than expected. We are concerned that difficulty in accessing post-exposure prophylaxis (PEP) rabies vaccine may be partly responsible for this rise. We advocate: (a) prompt course of active immunisation for all patients with significant exposure to proven or suspected rabid animals. (b) the use of an intradermal immunisation regime that requires a smaller quantity of the vaccine than the intramuscular regime and gives a better antibody response. (c) improved dog rabies control measures.
Resumo:
Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.