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Increased rumination in dairy cattle has been associated with increased saliva production and improved rumen health. Most estimates of rumination are based on direct visual observations. Recently, an electronic system was developed that allows for automated monitoring of rumination in cattle. The objective was to validate the data generated by this electronic (Hi-Tag, SCR Engineers Ltd., Netanya, Israel) rumination monitoring system. Assessments of 2 independent observers were highly correlated (r=0.99, n=23), indicating that direct human observations were suitable as the reference method. Measures from the Hi-Tag electronic system were validated by comparing values with those from a human observer for fifty-one 2-h observation periods from 27 Holstein cows. Rumination times (35.1±3.2min) from the electronic system were highly correlated with those from direct observation (r=0.93, R2=0.87, n=51), indicating that the electronic system was an accurate tool for monitoring this behavior in dairy cows.

There is currently no universally recommended and accepted method of data processing within the science of indirect calorimetry for either mixing chamber or breath-by-breath systems of expired gas analysis. Exercise physiologists were first surveyed to determine methods used to process oxygen consumption (V̇O 2 ) data, and current attitudes to data processing within the science of indirect calorimetry. Breath-by-breath datasets obtained from indirect calorimetry during incremental exercise were then used to demonstrate the consequences of commonly used time, breath and digital filter post-acquisition data processing strategies. Assessment of the variability in breath-by-breath data was determined using multiple regression based on the independent variables ventilation (VE), and the expired gas fractions for oxygen and carbon dioxide, FEO 2 and FECO 2 , respectively. Based on the results of explanation of variance of the breath-by-breath V̇O 2 data, methods of processing to remove variability were proposed for time-averaged, breath averaged and digital filter applications. Among exercise physiologists, the strategy used to remove the variability in sequential V̇O 2 measurements varied widely, and consisted of time averages (30 sec [38%], 60 sec [18%], 20 sec [11%], 15 sec [8%]), a moving average of five to 11 breaths (10%), and the middle five of seven breaths (7%). Most respondents indicated that they used multiple criteria to establish maximum V̇O 2 (V̇O 2max ) including: the attainment of age-predicted maximum heart rate (HR max ) [53%], respiratory exchange ratio (RER) >1.10 (49%) or RER >1.15 (27%) and a rating of perceived exertion (RPE) of >17, 18 or 19 (20%). The reasons stated for these strategies included their own beliefs (32%), what they were taught (26%), what they read in research articles (22%), tradition (13%) and the influence of their colleagues (7%). The combination of VE, FEO 2 and FECO 2 removed 96–98% of V̇O 2 breath-by-breath variability in incremental and steady-state exercise V̇O 2 data sets, respectively. Correction of residual error in V̇O 2 datasets to 10% of the raw variability results from application of a 30-second time average, 15-breath running average, or a 0.04 Hz low cut-off digital filter. Thus, we recommend that once these data processing strategies are used, the peak or maximal value becomes the highest processed datapoint. Exercise physiologists need to agree on, and continually refine through empirical research, a consistent process for analysing data from indirect calorimetry.

Background Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other. Methods We compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level. Results The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%). Conclusions On an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.

Background Hospital discharge summaries are a key communication tool ensuring continuity of care between primary and secondary care. Incomplete or untimely communication of information increases risk of hospital readmission and associated complications. The aim of this study was to evaluate whether the introduction of a new electronic discharge system (NewEDS) was associated with improvements in the completeness and timeliness of discharge information, in Nottingham University Hospitals NHS Trust, England. Methods A before and after longitudinal study design was used. Data were collected using the gold standard auditing tool from the Royal College of Physicians (RCP). This tool contains a checklist of 57 items grouped into seven categories, 28 of which are classified as mandatory by RCP. Percentage completeness (out of the 28 mandatory items) was considered to be the primary outcome measure. Data from 773 patients discharged directly from the acute medical unit over eight-week long time periods (four before and four after the change to the NewEDS) from August 2010 to May 2012 were extracted and evaluated. Results were summarised by effect size on completeness before and after changeover to NewEDS respectively. The primary outcome variable was represented with percentage of completeness score and a non-parametric technique was used to compare pre-NewEDS and post-NewEDS scores. Results The changeover to the NewEDS resulted in an increased completeness of discharge summaries from 60.7% to 75.0% ( p  < 0.001) and the proportion of summaries created under 24 h from discharge increased significantly from 78.0% to 93.0% ( p  < 0.001). Furthermore, five of the seven grouped checklist categories also showed significant improvements in levels of completeness ( p  < 0.001), although there were reduced levels of completeness for three items (p < 0.001). Conclusion The introduction of a NewEDS was associated with a significant improvement in the completeness and timeliness of hospital discharge communication.

The objectives of the current study were 1) to validate the IceTag (http://www.icerobotics.com) automatic recording device for measuring lying, standing, and moving behavior in dairy calves, and 2) to improve the information yield from this device by applying a filtering procedure allowing for the detection of lying versus upright. The IceTag device provides measures of intensity (I) of lying, standing, and activity measured as percent lying, percent standing, and percent active, but does not directly measure lying, standing, and moving behavior because body movements occurring while lying (e.g., shifts in lying position) and while upright (e.g., grooming) are recorded as activity. Therefore, the following 3-step procedure was applied. First, thresholds for I were determined by choosing the cutoff that maximized the sum of sensitivity (Se) and specificity (Sp). Second, a lying period criterion (LPC) was established empirically, and IceTag data were filtered according to the LPC, providing information on the posture of the animal as lying versus being upright. Third, a new threshold of I was estimated for moving activity conditional on the animal being upright. IceTag recordings from 9 calves were compared with video recordings during a 12-h period and analyzed using 2 × 2 contingency tables. Data from the first 4 calves were used to determine an LPC, whereas the remaining 5 calves served for validation of the procedure. An optimal LPC was found by modeling the deviance between IceTag and video recordings as a function of the LPC and choosing the LPC threshold that minimized the deviance. The IceTag device was found to accurately measure the high-prevalence behaviors (lying and standing; Se+Sp >1.90) and less accurately measure the low-prevalence behavior (moving; Se+Sp=1.39). Application of the 3-step procedure using an optimal LPC estimate of 24.8s resulted in an improved description of calf behavior, yielding a valid representation of the number and duration of lying and upright periods (Se+Sp=2.00) within a precision of 0 to 49s (95% confidence interval). In group-housed dairy calves, valid measures of the number and duration of lying and upright periods may be obtained from the IceTag device when applying the presented filtering procedure to the data. Measures regarding locomotion, on the other hand, should be used with caution.

Voluntary work alone cannot sustain the assessments carried out by the Intergovernmental Panel on Climate Change. Thomas F. Stocker and Gian-Kasper Plattner call for institutional support and a longer report cycle.

Bar-code medication administration (BCMA) effectiveness is contingent upon compliance with best-practice protocols. We developed a 4-phased BCMA evaluation program to evaluate the degree of integration of current evidence into BCMA policies, procedures, and practices; identify barriers to best-practice BCMA use; and modify BCMA practice in concert with changes to the practice environment. This program provides an infrastructure for frontline nurses to partner with hospital leaders to continually evaluate and improve BCMA using a systematic process.

Background Record linkage techniques are widely used to enable health researchers to gain event based longitudinal information for entire populations. The task of record linkage is increasingly being undertaken by specialised linkage units (SLUs). In addition to the complexity of undertaking probabilistic record linkage, these units face additional technical challenges in providing record linkage ‘as a service’ for research. The extent of this functionality, and approaches to solving these issues, has had little focus in the record linkage literature. Few, if any, of the record linkage packages or systems currently used by SLUs include the full range of functions required. Methods This paper identifies and discusses some of the functions that are required or undertaken by SLUs in the provision of record linkage services. These include managing routine, on-going linkage; storing and handling changing data; handling different linkage scenarios; accommodating ever increasing datasets. Automated linkage processes are one way of ensuring consistency of results and scalability of service. Results Alternative solutions to some of these challenges are presented. By maintaining a full history of links, and storing pairwise information, many of the challenges around handling ‘open’ records, and providing automated managed extractions are solved. A number of these solutions were implemented as part of the development of the National Linkage System (NLS) by the Centre for Data Linkage (part of the Population Health Research Network) in Australia. Conclusions The demand for, and complexity of, linkage services is growing. This presents as a challenge to SLUs as they seek to service the varying needs of dozens of research projects annually. Linkage units need to be both flexible and scalable to meet this demand. It is hoped the solutions presented here can help mitigate these difficulties.

Measurement and calibration of an analog-to-digital converter (ADC) using a histogram-based method requires a large volume of data and a long test duration, especially for a high resolution ADC. A fast and accurate calibration method for pipelined ADCs is proposed in this research. The proposed calibration method composes histograms through the outputs of each stage and calculates error sources. The digitized outputs of a stage are influenced directly by the operation of the prior stage, so the results of the histogram provide the information of errors in the prior stage. The composed histograms reduce the required samples and thus calibration time being implemented by simple modules. For 14-bit resolution pipelined ADC, the measured maximum integral non-linearity (INL) is improved from 6.78 to 0.52 LSB, and the spurious-free dynamic range (SFDR) and signal-to-noise-and-distortion ratio (SNDR) are improved from 67.0 to 106.2dB and from 65.6 to 84.8dB, respectively.