Harbour porpoises (Phocoena phocoena) and minke whales (Balaenoptera acutorostrata) observed during land-based surveys in The Minch, north-west Scotland

Peer reviewedPublisher PD

Crowdsourcing step-by-step information extraction to enhance existing how-to videos

Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations. We first performed a formative study to verify that annotations are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player. To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It extracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We introduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text processing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customization, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77% precision and 81% recall

Awareness of fetal movements and care package to reduce fetal mortality (AFFIRM)::a trial-based and model-based cost-effectiveness analysis from a stepped wedge, cluster-randomised trial

Abstract Background The AFFIRM intervention aimed to reduce stillbirth and neonatal deaths by increasing awareness of reduced fetal movements (RFM) and implementing a care pathway when women present with RFM. Although there is uncertainty regarding the clinical effectiveness of the intervention, the aim of this analysis was to evaluate the cost-effectiveness. Methods A stepped-wedge, cluster-randomised trial was conducted in thirty-three hospitals in the United Kingdom (UK) and Ireland. All women giving birth at the study sites during the analysis period were included in the study. The costs associated with implementing the intervention were estimated from audits of RFM attendances and electronic healthcare records. Trial data were used to estimate a cost per stillbirth prevented was for AFFIRM versus standard care. A decision analytic model was used to estimate the costs and number of perinatal deaths (stillbirths + early neonatal deaths) prevented if AFFIRM were rolled out across Great Britain for one year. Key assumptions were explored in sensitivity analyses. Results Direct costs to implement AFFIRM were an estimated £95,126 per 1,000 births. Compared to standard care, the cost per stillbirth prevented was estimated to be between £86,478 and being dominated (higher costs, no benefit). The estimated healthcare budget impact of implementing AFFIRM across Great Britain was a cost increase of £61,851,400/year. Conclusions Perinatal deaths are relatively rare events in the UK which can increase uncertainty in economic evaluations. This evaluation estimated a plausible range of costs to prevent baby deaths which can inform policy decisions in maternity services. Trial registration The trial was registered with www.ClinicalTrials.gov, number NCT01777022. </jats:sec

Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis

Feline infectious peritonitis (FIP) is a severe feline coronavirus-associated syndrome in cats, which is invariably fatal without anti-viral treatment. In the majority of non-effusive FIP cases encountered in practice, confirmatory diagnostic testing is not undertaken and reliance is given to the interpretation of valuable, but essentially non-specific, clinical signs and laboratory markers. We hypothesised that it may be feasible to develop a machine learning (ML) approach which may be applied to the analysis of clinical data to aid in the diagnosis of disease. A dataset encompassing 1939 suspected FIP cases was scored for clinical suspicion of FIP on the basis of history, signalment, clinical signs and laboratory results, using published guidelines, comprising 683 FIP (35.2%), and 1256 non-FIP (64.8%) cases. This dataset was used to train, validate and evaluate two diagnostic machine learning ensemble models. These models, which analysed signalment and laboratory data alone, allowed the accurate discrimination of FIP and non-FIP cases in line with expert opinion. To evaluate whether these models may have value as a diagnostic tool, they were applied to a collection of 80 cases for which the FIP status had been confirmed (FIP: n = 58 (72.5%), non–FIP: n = 22 (27.5%)). Both ensemble models detected FIP with an accuracy of 97.5%, an area under the curve (AUC) of 0.969, sensitivity of 95.45% and specificity of 98.28%. This work demonstrates that, in principle, ML can be usefully applied to the diagnosis of non-effusive FIP. Further work is required before ML may be deployed in the laboratory as a diagnostic tool, such as training models on datasets of confirmed cases and accounting for inter-laboratory variation. Nevertheless, these results illustrate the potential benefit of applying ML to standardising and accelerating the interpretation of clinical pathology data, thereby improving the diagnostic utility of existing laboratory tests

Socioeconomic status, comorbidity, and mortality in patients with type 2 diabetes mellitus in Scotland 2004-2011: a cohort study

BACKGROUND: Mortality in people with and without diabetes often exhibits marked social patterning, risk of death being greater in deprived groups. This may reflect deprivation-related differences in comorbid disease (conditions additional to diabetes itself). This study sought to determine whether the social patterning of mortality in a population with type 2 diabetes mellitus (T2DM) is explained by differential comorbidity. METHODS: Hospital records for 70 197 men and 56 451 women diagnosed with T2DM at 25 years of age and above in Scotland during the period 2004–2011 were used to construct comorbidity histories. Sex-specific logistic models were fitted to predict mortality at 1 year after diagnosis with T2DM, predicted initially by age and socioeconomic status (SES) then extended to incorporate in turn 5 representations of comorbidity (including the Charlson Index). The capacity of comorbidity to explain social mortality gradients was assessed by observing the change in regression coefficients for SES following the addition of comorbidity. RESULTS: After adjustment for age and Charlson Index, the OR for the contrast between the least deprived and most deprived quintiles of SES for men was 0.79 (95% CI 0.67 to 0.94). For women, the OR was 0.81 (0.67 to 0.97). Similar results were obtained for the 4 other comorbidity measures used. CONCLUSIONS: The social patterning of mortality in people with T2DM is not fully explained by differing levels of comorbid disease additional to T2DM itself. Other dimensions of deprivation are implicated in the elevated death rates observed in deprived groups of people with T2DM

Decreases in Antimicrobial Use Associated With Multihospital Implementation of Electronic Antimicrobial Stewardship Tools.

BackgroundAntimicrobial stewards may benefit from comparative data to inform interventions that promote optimal inpatient antimicrobial use.MethodsAntimicrobial stewards from 8 geographically dispersed Veterans Affairs (VA) inpatient facilities participated in the development of antimicrobial use visualization tools that allowed for comparison to facilities of similar complexity. The visualization tools consisted of an interactive web-based antimicrobial dashboard and, later, a standardized antimicrobial usage report updated at user-selected intervals. Stewards participated in monthly learning collaboratives. The percent change in average monthly antimicrobial use (all antimicrobial agents, anti-methicillin-resistant Staphylococcus aureus [anti-MRSA] agents, and antipseudomonal agents) was analyzed using a pre-post (January 2014-January 2016 vs July 2016-January 2018) design with segmented regression and external comparison with uninvolved control facilities (n = 118).ResultsIntervention sites demonstrated a 2.1% decrease (95% confidence interval [CI], -5.7% to 1.6%) in total antimicrobial use pre-post intervention vs a 2.5% increase (95% CI, 0.8% to 4.1%) in nonintervention sites (absolute difference, 4.6%; P = .025). Anti-MRSA antimicrobial use decreased 11.3% (95% CI, -16.0% to -6.3%) at intervention sites vs a 6.6% decrease (95% CI, -9.1% to -3.9%) at nonintervention sites (absolute difference, 4.7%; P = .092). Antipseudomonal antimicrobial use decreased 3.4% (95% CI, -8.2% to 1.7%) at intervention sites vs a 3.6% increase (95% CI, 0.8% to 6.5%) at nonintervention sites (absolute difference, 7.0%; P = .018).ConclusionsComparative data visualization tool use by stewards at 8 VA facilities was associated with significant reductions in overall antimicrobial and antipseudomonal use relative to uninvolved facilities

Enhancing effective healthcare communication in Australia and Aotearoa New Zealand: Considerations for research, teaching, policy, and practice.

OBJECTIVE In this article we present a conceptual framework for enhancing effective healthcare communication in Australia and Aotearoa New Zealand. METHODS Through an iterative, deliberative dialogue approach, we, as experts from a variety of health professions and academic disciplines, worked together to identify core values and considerations for healthcare communication across numerous health professions and disciplines and within research, teaching, policy, and practice contexts. RESULTS The framework developed includes five core values at its centre: equitable, inclusive, evidence-based, collaborative, reflective. Around this are concentric circles showing key elements of collaborators, modality, context, and purpose. Each of these is explored. CONCLUSION This work may support benchmarking for healthcare providers, researchers, policymakers, and educators across a breadth of professions to help improve communication in clinical practice. The framework will also help to identify areas across disciplines that are shared and potentially idiosyncratic for various professions to promote interprofessional recognition, education, and collaboration. INNOVATION This framework is designed to start conversations, to form the foundation of a dialogue about the priorities and key considerations for developing teaching curricula, professional development, and research programs related to healthcare communication, providing a set of values specifically for the unique contexts of Australia and Aotearoa New Zealand. It can also be used to guide interdisciplinary healthcare professionals in advancing research, teaching, policy, and practice related to healthcare communication

Early phase clinical trials extension to the guidelines for the content of statistical analysis plans

This paper reports guidelines for the content of statistical analysis plans for early phase clinical trials, ensuring specification of the minimum reporting analysis requirements, by detailing extensions (11 new items) and modifications (25 items) to existing guidance after a review by various stakeholders