Survivorship care provision for patients with hematologic malignancies: The quest for quality evidence

This is an editorial that depicts the importance for developing more quality evidence to guide the survivorship care provision for patients with hematologic malignancies. Treatments for hematologic malignancies are often complex and debilitating, with increased risk of immune suppression and infections1. Some patients receive allogeneic stem cell transplantation that often requires in-patient stay of several weeks and life-long medical follow up. In recent years, advances in treatment regimens, and an aging population saw an increasing number of patients living with a hematologic malignancies or surviving curative therapy.2 The increased use of targeted therapies in hematologic malignancies (e.g. rituximab for non-Hodgkin lymphoma, bortezomib in multiple myeloma and imatinib in Chronic Myelogenous Leukemia has also resulted in improved overall survival...

Perceived safety, quality and cultural competency of maternity care for culturally and linguistically diverse women in Queensland

Various policies, plans, and initiatives have been implemented to provide safe, quality, and culturally competent care to patients within Queensland’s healthcare system. A series of models of maternity care are available in Queensland that range from standard public care to private midwifery care. The current study aimed to determine whether identifying as Culturally or Linguistically Diverse (CALD) was associated with the perceived safety, quality, and cultural competency of maternity care from a consumer perspective, and to identify specific needs and preferences of CALD maternity care consumers. Secondary analysis of data collected in the Having a Baby in Queensland Survey 2012 was used to compare the experiences of 655 CALD women to those of 4049 non-CALD women in Queensland, Australia, across three stages of maternity care: pregnancy, labour and birth, and after birth. After adjustment for model of maternity care received and socio-demographic characteristics, CALD women were significantly more likely than non-CALD women to experience suboptimal staff technical competence in pregnancy, overall perceived safety in pregnancy and labour/birth, and interpersonal sensitivity in pregnancy and labour/birth. Approximately 50% of CALD women did not have the choice to use a translator or interpreter, or the gender of their care provider, during labour and birth. Thirteen themes of preferences and needs of CALD maternity care consumers based on ethnicity, cultural beliefs, or traditions were identified, however, these were rarely met. Findings imply that CALD women in Queensland experience disadvantageous maternity care with regards to perceived staff technical competence, safety, and interpersonal sensitivity, and receive care that lacks cultural competence. Improved access to support persons, continuity and choice of carer, and staff availability and training is recommended.

A network DEA quantity and quality-orientated production model: An application to Australian university research services

The motivation for this analysis is the recently developed Excellence in Research for Australia (ERA) program developed to assess the quality of research in Australia. The objective is to develop an appropriate empirical model that better represents the underlying production of higher education research. In general, past studies on university research performance have used standard DEA models with some quantifiable research outputs. However, these suffer from the twin maladies of an inappropriate production specification and a lack of consideration of the quality of output. By including the qualitative attributes of peer-reviewed journals, we develop a procedure that captures both quality and quantity, and apply it using a network DEA model. Our main finding is that standard DEA models tend to overstate the research efficiency of most Australian universities.

Assessing testamentary and decision-making capacity: Approaches and models

The need for better and more accurate assessments of testamentary and decision-making capacity grows as Australian society ages and incidences of mentally disabling conditions increase. Capacity is a legal determination, but one on which medical opinion is increasingly being sought. The difficulties inherent within capacity assessments are exacerbated by the ad hoc approaches adopted by legal and medical professionals based on individual knowledge and skill, as well as the numerous assessment paradigms that exist. This can negatively affect the quality of assessments, and results in confusion as to the best way to assess capacity. This article begins by assessing the nature of capacity. The most common general assessment models used in Australia are then discussed, as are the practical challenges associated with capacity assessment. The article concludes by suggesting a way forward to satisfactorily assess legal capacity given the significant ramifications of getting it wrong.

Development of a method to assess the quality of ankle joint reduction in 3D

This project developed a quantitative method for determining the quality of the surgical alignment of the bone fragments after an ankle fracture. The research examined the feasibility of utilising MRI-based bone models versus the gold standard CT-based bone models in order to reduce the amount of ionising radiation the patient is exposed to. In doing so, the thesis reports that there is potential for MRI to be used instead of CT depending on the scanning parameters used to obtain the medical images, the distance of the implant relative to the joint surface, and the implant material.

Rank regression analysis of correlated water quality data from South East Queensland

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459-464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Models of care in geriatric oncology nursing

Objectives To review models of care for older adults with cancer, with a focus on the role of the oncology nurse in geriatric oncology care. International exemplars of geriatric oncology nursing care are discussed. Data source Published peer reviewed literature, web-based resources, professional society materials, and the authors' experience. Conclusion Nursing care for older patients with cancer is complex and requires integrating knowledge from multiple disciplines that blends the sciences of geriatrics, oncology, and nursing. and which recognizes the dimensions of quality of life. Implications for Nursing Practice: Oncology nurses can benefit from learning key skills of comprehensive geriatric screening and assessment to improve the care they provide for older adults with cancer.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

A simple and fast two-locus quality control test to detect false positives due to batch effects in genome-wide association studies

The impact of erroneous genotypes having passed standard quality control (QC) can be severe in genome-wide association studies, genotype imputation, and estimation of heritability and prediction of genetic risk based on single nucleotide polymorphisms (SNP). To detect such genotyping errors, a simple two-locus QC method, based on the difference in test statistic of association between single SNPs and pairs of SNPs, was developed and applied. The proposed approach could detect many problematic SNPs with statistical significance even when standard single SNP QC analyses fail to detect them in real data. Depending on the data set used, the number of erroneous SNPs that were not filtered out by standard single SNP QC but detected by the proposed approach varied from a few hundred to thousands. Using simulated data, it was shown that the proposed method was powerful and performed better than other tested existing methods. The power of the proposed approach to detect erroneous genotypes was approximately 80% for a 3% error rate per SNP. This novel QC approach is easy to implement and computationally efficient, and can lead to a better quality of genotypes for subsequent genotype-phenotype investigations.

Monitoring healing progression and characterizing the mechanical environment in preclinical models for bone tissue engineering

The treatment of large segmental bone defects remains a significant clinical challenge. Due to limitations surrounding the use of bone grafts, tissue-engineered constructs for the repair of large bone defects could offer an alternative. Before translation of any newly developed tissue engineering (TE) approach to the clinic, efficacy of the treatment must be shown in a validated preclinical large animal model. Currently, biomechanical testing, histology, and microcomputed tomography are performed to assess the quality and quantity of the regenerated bone. However, in vivo monitoring of the progression of healing is seldom performed, which could reveal important information regarding time to restoration of mechanical function and acceleration of regeneration. Furthermore, since the mechanical environment is known to influence bone regeneration, and limb loading of the animals can poorly be controlled, characterizing activity and load history could provide the ability to explain variability in the acquired data sets and potentially outliers based on abnormal loading. Many approaches have been devised to monitor the progression of healing and characterize the mechanical environment in fracture healing studies. In this article, we review previous methods and share results of recent work of our group toward developing and implementing a comprehensive biomechanical monitoring system to study bone regeneration in preclinical TE studies.

Drying characteristics and quality of grape under physical pretreatment

Grape drying is a slow and energy intensive process because the waxy peel has low permeability to moisture. Therefore, peel chemical and physical pretreatments are considered before drying in order to facilitate water diffusion. However, they cause heterogeneity in the waxes removal and problems during shelf-life. In this paper an alternative abrasive pretreatment of grape peel, for enhancing the drying rate and preserving the samples, was applied to Red Globe grapes. Convective drying experiments were carried out at 40-70 Centigrade and at 2.3 m/s air velocity. The effect of wax abrasive pretreatment on the drying kinetics and quality parameters of raisins was investigated. The results were compared with those of samples pretreated by dipping in alkaline ethyl oleate solution and untreated grapes. All the dried samples are darker than fresh one and shrunked. The samples pretreated by peel abrasion and dried at 50 centigrade showed the lowest color changes, less shrinkage and the best rehydration capacity. The drying kinetics and shrinkage curves were also analyzed using some commonly available empirical models.