Exploring how support workers understand their role in supporting adults with intellectual disabilities to access the Internet for intimate relationships.

BACKGROUND: Some adults with intellectual disabilities need support to access the Internet. This study explores how support workers understand their role in facilitating Internet access for intimate relationships. METHOD: Eight support workers in the West Midlands of the UK were interviewed face-to-face, using semi-structured interviews and a thematic analysis was used to interpret the data. RESULTS: Three main themes emerged; social and organizational dilemmas (including sub-themes of; role and moral positioning, expectations of support, and protected and reflective space), power and position and policy dilemmas. CONCLUSION: Support workers said that adults with intellectual disabilities should have access to the Internet for intimate relationships. There was a range of views on whether it was their job to support this. A lack of training in Internet use was highlighted.

Enhanced recovery and early extubation after pediatric cardiac surgery using single-dose intravenous methadone.

Background/Aims: Methadone may offer advantages in facilitating early extubation after cardiac surgery, but very few data are available in the pediatric population. Setting/Design: Community tertiary children's hospital, retrospective case series. Materials and Methods: We performed a retrospective analysis of all pediatric cardiac surgical patients for whom early extubation was intended. A multimodal analgesic regimen was used for all patients, consisting of methadone (0.2-0.3 mg/kg), ketamine (0.5 mg/kg plus 0.25 mg/kg/h), lidocaine (1 mg/kg plus 1.5 mg/kg/h), acetaminophen (15 mg/kg), and parasternal ropivacaine (0.5 mL/kg of 0.2%). Outcome variables were collected with descriptive statistics. Results: A total of 24 children [median = 7 (interquartile range = 3.75-13.75) years old, 23.7 (14.8-53.4) kg] were included in the study; 22 (92%) had procedures performed on bypass and 11 (46%) involved a reentry sternotomy. Methadone dosing was 0.26 (0.23-0.29) mg/kg. None of the children required intraoperative supplemental opioids; 23 (96%) were extubated in the operating room. The first paCO2 on pediatric intensive care unit admission was 51 (45-58) mmHg. Time to first supplemental opioid administration was 5.1 (3.5-9.5) h. Cumulative total supplemental opioids (in intravenous morphine equivalents) at 24 and 72 h were 0.2 (0.09-0.32) and 0.42 (0.27-0.68) mg/kg. One child required postoperative bilevel positive airway pressure support, but none required reintubation. None had pruritus; three (13%) experienced nausea. Conclusion: A methadone-based multimodal regimen facilitated early extubation without appreciable adverse events. Further investigations are needed to confirm efficacy of this regimen and to assess whether the excellent safety profile seen here holds in the hands of multiple providers caring for a larger, more heterogeneous population.

A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates.

Our hypothesis is that building ensembles of small sets of strong classifiers constructed with different learning algorithms is, on average, the best approach to classification for real-world problems. We propose a simple mechanism for building small heterogeneous ensembles based on exponentially weighting the probability estimates of the base classifiers with an estimate of the accuracy formed through cross-validation on the train data. We demonstrate through extensive experimentation that, given the same small set of base classifiers, this method has measurable benefits over commonly used alternative weighting, selection or meta-classifier approaches to heterogeneous ensembles. We also show how an ensemble of five well-known, fast classifiers can produce an ensemble that is not significantly worse than large homogeneous ensembles and tuned individual classifiers on datasets from the UCI archive. We provide evidence that the performance of the cross-validation accuracy weighted probabilistic ensemble (CAWPE) generalises to a completely separate set of datasets, the UCR time series classification archive, and we also demonstrate that our ensemble technique can significantly improve the state-of-the-art classifier for this problem domain. We investigate the performance in more detail, and find that the improvement is most marked in problems with smaller train sets. We perform a sensitivity analysis and an ablation study to demonstrate the robustness of the ensemble and the significant contribution of each design element of the classifier. We conclude that it is, on average, better to ensemble strong classifiers with a weighting scheme rather than perform extensive tuning and that CAWPE is a sensible starting point for combining classifiers.

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances.

In the last 5 years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only nine of these algorithms are significantly more accurate than both benchmarks and that one classifier, the collective of transformation ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more robust testing of new algorithms in the future.

Impact of standardization of antimicrobial prophylaxis duration in pediatric cardiac surgery.

OBJECTIVES: The optimal duration of antimicrobial prophylaxis following pediatric cardiac surgery is still debated. Adult studies suggest that shorter durations are adequate, but there is a paucity of data on pediatric patients. METHODS: This quasi-experimental study reviewed the charts of patients 18 years and younger who underwent cardiac surgery from April 2011 to November 2014 at a single institution. Starting in April 2013, a protocol was implemented to limit antimicrobial prophylaxis to 48 hours following sternal closure. Two analyses were performed: (1) identification of risk factors for surgical site infections from the entire cohort, and (2) comparison of surgical site infection incidence in the pre- and postprotocol groups. RESULTS: In the entire cohort, delayed sternal closure (adjusted odds ratio [OR], 5.7; 95% confidence interval [CI], 1.8-17.9) and younger age (adjusted OR, 2.1; 95% CI, 1.1-3.8) were associated with incidence of surgical site infection. Following the protocol change, duration of antimicrobial prophylaxis decreased from 4.2 ± 2.7 to 1.9 ± 1.3 days (P < .0001). After adjusting for age and delayed sternal closure, the postprotocol group had an adjusted OR of 0.98 (95% CI, 0.32-3.00) for occurrence of surgical site infection. Other outcomes were not altered following the protocol change. CONCLUSIONS: Restricting antimicrobial prophylaxis to 48 hours following pediatric cardiac surgery did not increase the incidence of surgical site infection at our institution. Further study is needed to validate this finding and to identify practices that reduce surgical site infections in those with delayed sternal closure.

On the segmentation and classification of hand radiographs.

This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components.