ATAR scores only part of the picture for teaching

A common debate has resurfaced over teacher quality and the quality of teacher education in Australia. This time it was started by a leaked draft report into teacher education from the Australian Institute of Teaching and School Leadership (AITSL) which the public can’t expect to see for at least another month. Media reports have all focused on one aspect of the wide-ranging report: teaching students' ATARs.

Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

The ability of Coupled General Circulation Models (CGCMs) participating in the Intergovernmental Panel for Climate Change's fourth assessment report (IPCC AR4) for the 20th century climate (20C3M scenario) to simulate the daily precipitation over the Indian region is explored. The skill is evaluated on a 2.5A degrees x 2.5A degrees grid square compared with the Indian Meteorological Department's (IMD) gridded dataset, and every GCM is ranked for each of these grids based on its skill score. Skill scores (SSs) are estimated from the probability density functions (PDFs) obtained from observed IMD datasets and GCM simulations. The methodology takes into account (high) extreme precipitation events simulated by GCMs. The results are analyzed and presented for three categories and six zones. The three categories are the monsoon season (JJASO - June to October), non-monsoon season (JFMAMND - January to May, November, December) and for the entire year (''Annual''). The six precipitation zones are peninsular, west central, northwest, northeast, central northeast India, and the hilly region. Sensitivity analysis was performed for three spatial scales, 2.5A degrees grid square, zones, and all of India, in the three categories. The models were ranked based on the SS. The category JFMAMND had a higher SS than the JJASO category. The northwest zone had higher SSs, whereas the peninsular and hilly regions had lower SS. No single GCM can be identified as the best for all categories and zones. Some models consistently outperformed the model ensemble, and one model had particularly poor performance. Results show that most models underestimated the daily precipitation rates in the 0-1 mm/day range and overestimated it in the 1-15 mm/day range.

A confidence-based approach for improving keyword hypothesis scores

The task in keyword spotting (KWS) is to hypothesise times at which any of a set of key terms occurs in audio. An important aspect of such systems are the scores assigned to these hypotheses, the accuracy of which have a significant impact on performance. Estimating these scores may be formulated as a confidence estimation problem, where a measure of confidence is assigned to each key term hypothesis. In this work, a set of discriminative features is defined, and combined using a conditional random field (CRF) model for improved confidence estimation. An extension to this model to directly address the problem of score normalisation across key terms is also introduced. The implicit score normalisation which results from applying this approach to separate systems in a hybrid configuration yields further benefits. Results are presented which show notable improvements in KWS performance using the techniques presented in this work. © 2013 IEEE.

Syntax-Based Analytic Reading of Musical Scores

As part of a larger research project in musical structure, a program has been written which "reads" scores encoded in an input language isomorphic to music notation. The program is believed to be the first of its kind. From a small number of parsing rules the program derives complex configurations, each of which is associated with a set of reference points in a numerical representation of a time-continuum. The logical structure of the program is such that all and only the defined classes of events are represented in the output. Because the basis of the program is syntactic (in the sense that parsing operations are performed on formal structures in the input string), many extensions and refinements can be made without excessive difficulty. The program can be applied to any music which can be represented in the input language. At present, however, it constitutes the first stage in the development of a set of analytic tools for the study of so-called atonal music, the revolutionary and little understood music which has exerted a decisive influence upon contemporary practice of the art. The program and the approach to automatic data-structuring may be of interest to linguists and scholars in other fields concerned with basic studies of complex structures produced by human beings.

Credit scores, cardiovascular disease risk, and human capital.

Credit scores are the most widely used instruments to assess whether or not a person is a financial risk. Credit scoring has been so successful that it has expanded beyond lending and into our everyday lives, even to inform how insurers evaluate our health. The pervasive application of credit scoring has outpaced knowledge about why credit scores are such useful indicators of individual behavior. Here we test if the same factors that lead to poor credit scores also lead to poor health. Following the Dunedin (New Zealand) Longitudinal Study cohort of 1,037 study members, we examined the association between credit scores and cardiovascular disease risk and the underlying factors that account for this association. We find that credit scores are negatively correlated with cardiovascular disease risk. Variation in household income was not sufficient to account for this association. Rather, individual differences in human capital factors—educational attainment, cognitive ability, and self-control—predicted both credit scores and cardiovascular disease risk and accounted for ∼45% of the correlation between credit scores and cardiovascular disease risk. Tracing human capital factors back to their childhood antecedents revealed that the characteristic attitudes, behaviors, and competencies children develop in their first decade of life account for a significant portion (∼22%) of the link between credit scores and cardiovascular disease risk at midlife. We discuss the implications of these findings for policy debates about data privacy, financial literacy, and early childhood interventions.

Validation of ICDPIC software injury severity scores using a large regional trauma registry.

BACKGROUND: Administrative or quality improvement registries may or may not contain the elements needed for investigations by trauma researchers. International Classification of Diseases Program for Injury Categorisation (ICDPIC), a statistical program available through Stata, is a powerful tool that can extract injury severity scores from ICD-9-CM codes. We conducted a validation study for use of the ICDPIC in trauma research. METHODS: We conducted a retrospective cohort validation study of 40,418 patients with injury using a large regional trauma registry. ICDPIC-generated AIS scores for each body region were compared with trauma registry AIS scores (gold standard) in adult and paediatric populations. A separate analysis was conducted among patients with traumatic brain injury (TBI) comparing the ICDPIC tool with ICD-9-CM embedded severity codes. Performance in characterising overall injury severity, by the ISS, was also assessed. RESULTS: The ICDPIC tool generated substantial correlations in thoracic and abdominal trauma (weighted κ 0.87-0.92), and in head and neck trauma (weighted κ 0.76-0.83). The ICDPIC tool captured TBI severity better than ICD-9-CM code embedded severity and offered the advantage of generating a severity value for every patient (rather than having missing data). Its ability to produce an accurate severity score was consistent within each body region as well as overall. CONCLUSIONS: The ICDPIC tool performs well in classifying injury severity and is superior to ICD-9-CM embedded severity for TBI. Use of ICDPIC demonstrates substantial efficiency and may be a preferred tool in determining injury severity for large trauma datasets, provided researchers understand its limitations and take caution when examining smaller trauma datasets.

Utilization of analgesics, sedatives, and pain scores in infants with a prolonged hospitlaization: A prospective descriptive cohort study

Aim
Describe the utilization of analgesic and sedative medications and documentation of pain scores in a cohort of critically ill infants in a neonatal intensive care unit.

Method
A prospective, longitudinal, cohort study of infants with a predicted length of stay =28 days. Dosages and routes of administration of analgesic and sedative medications and documentation of pain scores were collected on a daily basis.

Results
55 infants were enrolled into the study. Oral sucrose was administered to all 55 infants, 51 infants (93%) were administered enteral acetaminophen and 50 (91%) infants were administered morphine during their hospitalization. Sedatives were administered to 42 infants (76%); 36 (65%) were administered chloral hydrate and 32 (58%) were administered intravenous midazolam. With the exception of the first week of admission, when there was highest utilization of opioids and lower use of sucrose, acetaminophen and sedatives, the pattern of administration of analgesic and sedative agents remained relatively constant throughout the hospitalization. Pain scores were documented for 36 (65%) infants during their hospitalisation, however for these 36 infants, pain scores were infrequently recorded.

Conclusion
There was substantial and varied analgesic and sedative use in this cohort of infants, yet infrequent documentation of pain assessment scores. These practices highlight important clinical implications for sick infants requiring careful consideration of pain and distress management.

The effect of statistics anxiety on students’ predicted and actual test scores

This study investigated the effect of statistics anxiety and attitudes on first year psychology students’ predicted and actual statistics class test scores. A total of 52 students completed the Statistics Anxiety Rating Scale and estimated their class test scores one week before their test at the end of first year. Regression models were conducted with the six attitude and anxiety subscales as predictors and the predicted and actual test scores as criterion variables. The results showed that computation self concept and fear of asking for help accounted for 37% of the variance in predicted test scores. However, when actual test scores were analysed the significant predictors were worth of statistics and interpretation anxiety, which accounted for 20% of the variance. These results suggested that while statistics anxiety does influence students’ perceptions of their competence it appears to have less effect on their actual performance. Results also suggested that students were unaware of their own statistical competence. Remedial action is required to address the level of statistics anxiety experienced by first year undergraduate psychology students, as it appears to result in unrealistic assessments of their ability and has detrimental effects on their statistics self-efficacy.

Ranking of microRNA target prediction scores by Pareto front analysis

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure. which encourages further research towards a higher-dimensional analysis of Pareto fronts. (C) 2010 Elsevier Ltd. All rights reserved.

The Uncertainty of Experience: On George Brecht's Event Scores

George Brecht, an artist best known for his associations with Fluxus, is considered to have made significant contributions to emerging traditions of conceptual art and experimental music in the early 1960s. His Event scores, brief verbal scores that comprised lists of terms or open-ended instructions, provided a signature model for indeterminate composition and were ‘used extensively by virtually every Fluxus artist’. This article revisits Brecht’s early writings and research to argue that, while Event scores were adopted within Fluxus performance, they were intended as much more than performance devices. Specifically, Brecht conceived of his works as ‘structures of experience’ that, by revealing the underlying connections between chanced forms, could enable a kind of enlightenment rooted within an experience of a ‘unified reality’.