Effect of changing data collection parameters on statistical motor unit number estimates

The effect of number of samples and selection of data for analysis on the calculation of surface motor unit potential (SMUP) size in the statistical method of motor unit number estimates (MUNE) was determined in 10 normal subjects and 10 with amyotrophic lateral sclerosis (ALS). We recorded 500 sequential compound muscle action potentials (CMAPs) at three different stable stimulus intensities (10–50% of maximal CMAP). Estimated mean SMUP sizes were calculated using Poisson statistical assumptions from the variance of 500 sequential CMAP obtained at each stimulus intensity. The results with the 500 data points were compared with smaller subsets from the same data set. The results using a range of 50–80% of the 500 data points were compared with the full 500. The effect of restricting analysis to data between 5–20% of the CMAP and to standard deviation limits was also assessed. No differences in mean SMUP size were found with stimulus intensity or use of different ranges of data. Consistency was improved with a greater sample number. Data within 5% of CMAP size gave both increased consistency and reduced mean SMUP size in many subjects, but excluded valid responses present at that stimulus intensity. These changes were more prominent in ALS patients in whom the presence of isolated SMUP responses was a striking difference from normal subjects. Noise, spurious data, and large SMUP limited the Poisson assumptions. When these factors are considered, consistent statistical MUNE can be calculated from a continuous sequence of data points. A 2 to 2.5 SD or 10% window are reasonable methods of limiting data for analysis. Muscle Nerve 27: 320–331, 2003

Skin cancer clinics in Australia: workload profile and performance indicators from an analysis of billing data

Objective: To describe the workload profile in a network of Australian skin cancer clinics. Design and setting: Analysis of billing data for the first 6 months of 2005 in a primary-care skin cancer clinic network, consisting of seven clinics and staffed by 20 doctors, located in the Northern Territory, Queensland and New South Wales. Main outcome measures: Consultation to biopsy ratio (CBR); biopsy to treatment ratio (BTR); number of benign naevi excised per melanoma (number needed to treat [NNT]). Results: Of 69780 billed activities, 34 622 (49.6%) were consultations, 19 358 (27.7%) biopsies, 8055 (11.5%) surgical excisions, 2804 (4.0%) additional surgical repairs, 1613 (2.3%) non-surgical treatments of cancers and 3328 (4.8%) treatments of premalignant or non-malignant lesions. A total of 6438 cancers were treated (116 melanomas by excision, 4709 non-melanoma skin cancers [NMSCs] by excision, and 1613 NMSCs non-surgically); 5251 (65.2%) surgical wounds were repaired by direct suture, 2651 (32.9%) by a flap (of which 44.8% were simple flaps), 42 (0.5%) by wedge excision and 111 (1.4%) by grafts. The CBR was 1.79, the BTR was 3.1 and the NNT was 28.6. Conclusions: In this network of Australian skin cancer clinics, one in three biopsies identified a skin cancer (BTR, 3.1), and about 29 benign lesions were excised per melanoma (NNT, 28.6). The estimated NNT was similar to that reported previously in general practice. More data are needed on health outcomes, including effectiveness of treatment and surgical repair.

A design pattern for efficient retrieval of large data sets from remote data sources

Retrieving large amounts of information over wide area networks, including the Internet, is problematic due to issues arising from latency of response, lack of direct memory access to data serving resources, and fault tolerance. This paper describes a design pattern for solving the issues of handling results from queries that return large amounts of data. Typically these queries would be made by a client process across a wide area network (or Internet), with one or more middle-tiers, to a relational database residing on a remote server. The solution involves implementing a combination of data retrieval strategies, including the use of iterators for traversing data sets and providing an appropriate level of abstraction to the client, double-buffering of data subsets, multi-threaded data retrieval, and query slicing. This design has recently been implemented and incorporated into the framework of a commercial software product developed at Oracle Corporation.

Data grid for the management, reconstruction, analysis and visualisation of archaeological data

In 2007 Associate Professor Jay Hall retires from the University of Queensland after more than 30 years of service to the Australian archaeological community. Celebrated as a gifted teacher and a pioneer of Queensland archaeology, Jay leaves a rich legacy of scholarship and achievement across a wide range of archaeological endeavours. An Archæological Life brings together past and present students, colleagues and friends to celebrate Jay’s contributions, influences and interests.

Integrating technology to improve the efficiency of qualitative data analysis - A note on methods

Qualitative data analysis (QDA) is often a time-consuming and laborious process usually involving the management of large quantities of textual data. Recently developed computer programs offer great advances in the efficiency of the processes of QDA. In this paper we report on an innovative use of a combination of extant computer software technologies to further enhance and simplify QDA. Used in appropriate circumstances, we believe that this innovation greatly enhances the speed with which theoretical and descriptive ideas can be abstracted from rich, complex, and chaotic qualitative data. © 2001 Human Sciences Press, Inc.

Testing insect movement theory: empirical analysis of pest data routinely collected from agricultural crops

Observations of an insect's movement lead to theory on the insect's flight behaviour and the role of movement in the species' population dynamics. This theory leads to predictions of the way the population changes in time under different conditions. If a hypothesis on movement predicts a specific change in the population, then the hypothesis can be tested against observations of population change. Routine pest monitoring of agricultural crops provides a convenient source of data for studying movement into a region and among fields within a region. Examples of the use of statistical and computational methods for testing hypotheses with such data are presented. The types of questions that can be addressed with these methods and the limitations of pest monitoring data when used for this purpose are discussed. (C) 2002 Elsevier Science B.V. All rights reserved.