The role of objective and subjective evaluation in the production and marketing of goats for meat

Objective and subjective evaluations of goats for meat production are related to important determinants of production and profitability. The most important attributes in assessment of goats for market are: live weight; body condition score; and the age of goats. As goats grow, their carcass and body organs increase in weight in proportion to the empty body weight. For farmers and field workers the linear regression approach for estimating carcass weight by measuring live weight is the most suitable as it accounts for 88 to 97% of the variation in carcass, offal and boneless meat weight. Live weight scales or heart girth tapes should be used and the risks and errors associated with these methods are summarized. The proportion of a live goat that is the carcass, known as dressing percentage, increases from 35% to about 50% as goats grow. The usefulness and errors associated with dressing percentage in field estimation are discussed. A valuable subjective method for estimating the nutritional status of goats is the use of body condition scoring as it accounts for 60 to 67% of the variation in live weight change, carcass weight and fat reserves of goats. A method for body condition scoring and a similar fat scoring system are explained. Body condition score is also associated with mortality risk and reproductive performance of goats. The number of permanent incisors in the lower jaw of goats is a method of estimating the age of goats but is biased by differences in live weights of goats. The value and role of ultrasound scanning the carcasses of goats is summarized. For the marketing of kid meat no permanent incisors should have erupted. Other useful practices for the successful marketing of goat meat are discussed including: knowing market specifications and chemical withholding periods; animal health; prevention of bruising; identification of goats; size of consignments; timeliness; provision of paperwork. A checklist is provided. The use of subjective and objective assessment techniques in evaluating goats for meat production will provide the best results. Where only subjective assessment techniques are available they will provide satisfactory performance provided the skills have been learnt and are applied.

Using linear programming for weights identification of generalized bonferroni means in R

The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset.

Development of an evidence-based scoring system (HF-IS) to assess the quality of heart failure programmes for patients postdischarge from hospital

Aim.  The aim of this study was to develop a potential scoring algorithm for interventions in a chronic heart failure management programme – the Heart Failure Intervention Score – to facilitate quality improvement and programme auditing.

Background.  The overall efficacy of chronic heart failure management programmes has been demonstrated in several meta-analyses. However, meta-analyses did not determine individual interventions in a programme that resulted in beneficial patient outcomes.

Design.  A prospective cross-sectional survey design.

Method.  All chronic heart failure management programmes in Australia (n = 62), identified by a national register, were surveyed to determine programme characteristics and interventions.

Results.  Of the 62 national chronic heart failure management programmes, 48 (77%) completed the survey and 27 individual interventions were identified. Variability in the use of the key interventions was common among the programmes. Each intervention was given an arbitrary weighted score according to the level of supportive evidence available and a total score calculated. Programmes were then categorised into low or high complexity based on several interventions implemented and their weighted score. A total score of ≥190 (median = 178, interquartile range 176–195) was used to divide programmes into two groups. Nine programmes were categorised into high Heart Failure Intervention Score group and majority of these were based in the acute hospital setting (78%). In the low Heart Failure Intervention Score group, there were 39 programmes of which there were a higher proportion of community-based programmes (38%) and programmes in small community hospitals (10%).

Conclusion.  The Heart Failure Intervention Score provides a potential evidence-based quality improvement tool through which a set of minimum standards can be developed. Implementation of the Heart Failure Intervention Score provides guidance to programme coordinators to enable monitoring of standards of heart failure programmes, which may potentially result in better patient outcomes.

Learning aggregation weights from 3-tuple comparison sets

An important task in multiple-criteria decision making is how to learn the weights and parameters of an aggregation function from empirical data. We consider this in the context of quantifying ecological diversity, where such data is to be obtained as a set of pairwise comparisons specifying that one community should be considered more diverse than another. A problem that arises is how to collect a sufficient amount of data for reliable model determination without overloading individuals with the number of comparisons they need to make. After providing an algorithm for determining criteria weights and an overall ranking from such information, we then investigate the improvement in accuracy if ranked 3-tuples are supplied instead of pairs. We found that aggregation models could be determined accurately from significantly fewer 3-tuple comparisons than pairs. © 2013 IEEE.

Post-anaesthetic discharge scoring criteria: key findings from a systematic review

Background
Patient safety depends on nurses' clinical judgment. In post-anaesthetic care, objective scoring systems are commonly used to help nurses assess when a patient is ready to go back to the ward or be discharged home after day surgery. Although there are several criteria used to assess patient readiness for discharge from the post-anaesthetic care unit, evaluation of the validity and reliability of these criteria is scarce.

Aims
This article presents key findings from a systematic review conducted to identify the essential components of an effective and feasible scoring system to assess patients following surgical anaesthesia for discharge from the post-anaesthetic care unit.

Methods
The protocol for the systematic review of quantitative studies investigating assessment criteria for discharge of adult patients from the post-anaesthetic care unit was approved by the Joanna Briggs Institute and conducted consistent with the methodology of the Institute. Twelve databases and grey literature, such as conference proceedings, were searched for published studies between 1970 and 2010. Two reviewers independently assessed study eligibility for inclusion. Reference lists of included studies were appraised.

Results
Eight studies met the inclusion criteria; only one was a randomised controlled trial. Variables identified as essential when assessing a patient's readiness for discharge from the post-anaesthetic care unit were conscious state, blood pressure, nausea and vomiting, and pain. Assessment of psychomotor and cognitive recovery and other vital signs were also identified as relevant variables to consider.

Conclusions
There was limited high-quality research regarding criteria to assess patient readiness for discharge from the post-anaesthetic unit. The key recommendations, with moderate to high risk of bias, include that assessment of specific variables (pain, conscious state, blood pressure, and nausea and vomiting) should be made before patient discharge. These key findings have informed a subsequent study to reach international consensus on effective assessment criteria and a project to test the clinical reliability of a tool for use by nurses in assessing patient readiness for discharge from post-anaesthetic care.

On some properties of weighted averaging with variable weights

Density-based means have been recently proposed as a method for dealing with outliers in the stream processing of data. Derived from a weighted arithmetic mean with variable weights that depend on the location of all data samples, these functions are not monotonic and hence cannot be classified as aggregation functions. In this article we establish the weak monotonicity of this class of averaging functions and use this to establish robust generalisations of these means. Specifically, we find that as proposed, the density based means are only robust to isolated outliers. However, by using penalty based formalisms of averaging functions and applying more sophisticated and robust density estimators, we are able to define a broader family of density based means that are more effective at filtering both isolated and clustered outliers. © 2014 Elsevier Inc. All rights reserved.

Basel risk weights, asset correlations, and book-to-market equity: evidence from Asian countries

We examine the effect of firm book-to-market equity values (BE/ME) on asset correlations which play an important role in determining risk weights under the current Basel capital requirements. Using firms in China, Hong Kong, Japan, Korea, Singapore and Taiwan over a sample period from 1988 to 2013, we find that BE/ME has a negative effect on asset correlations. This suggests a role for BE/ME as an additional factor in determining asset correlations, and thus risk weights, also potentially reducing incentives for regulatory capital arbitrage.

A new method for deriving priority weights by extracting consistent numerical-valued matrices from interval-valued fuzzy judgement matrix

It is important to derive priority weights from interval-valued fuzzy preferences when a pairwise comparative mechanism is used. By focusing on the significance of consistency in the pairwise comparison matrix, two numerical-valued consistent comparison matrices are extracted from an interval fuzzy judgement matrix. Both consistent matrices are derived by solving the linear or nonlinear programming models with the aid of assessments from Decision Makers (DMs). An interval priority weight vector from the extracted consistent matrices is generated. In order to retain more information hidden in the intervals, a new probability-based method for comparison of the interval priority weights is introduced. An algorithm for deriving the final priority interval weights for both consistent and inconsistent interval matrices is proposed. The algorithm is also generalized to handle the pairwise comparison matrix with fuzzy numbers. The comparative results from the five examples reveal that the proposed method, as compared with eight existing methods, exhibits a smaller degree of uncertainty pertaining to the priority weights, and is also more reliable based on the similarity measure. © 2014 Elsevier Inc. All rights reserved.

Biased experts and similarity based weights in preferences aggregation

In a group decision making setting, we consider the potential impact an expert can have on the overall ranking by providing a biased assessment of the alternatives that differs substantially from the majority opinion. In the framework of similarity based averaging functions, we show that some alternative approaches to weighting the experts' inputs during the aggregation process can minimize the influence the biased expert is able to exert.