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Abstract: The long‐standing debate into the potential benefit of developing mathematical thinking skills through learning to program has been reignited with the widespread introduction of programming in schools across many countries, including England where it is a statutory requirement for all pupils to be taught programming from 5 years old. Algorithm is introduced early in the English computing curriculum, yet there is limited knowledge of how young pupils view this concept. This paper explores pupils' (aged 10–11) understandings of algorithm following their engagement with 1 year of ScratchMaths, a curriculum designed to develop computational and mathematical thinking skills through learning to program. A total of 181 pupils from 6 schools undertook a set of written tasks to assess their interpretations and evaluations of different algorithms that solve the same problem, with a subset of these pupils subsequently interviewed to probe their understandings in greater depth. We discuss the different approaches identified, the evaluation criteria they used, and the aspects of the concept that pupils found intuitive or challenging, such as simplification and abstraction. The paper ends with some reflections on the implications of the research, concluding with a set of recommendations for pedagogy in developing primary pupils' algorithmic thinking. [ABSTRACT FROM AUTHOR]

Background Evidence suggests a correlation of blood pressure (BP) level with presence of diabetic microvascular complications (DMCs), but the effect of BP on DMCs incidence is not well-established. We aimed to explore the associations between BP and DMCs (diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy) risk in participants with diabetes. Methods This study included 23030 participants, free of any DMCs at baseline, from the UK Biobank. We applied multivariable-adjusted Cox regression models to estimate BP-DMCs association and constructed BP genetic risk scores (GRSs) to test their association with DMCs phenotypes. Differences in incidences of DMCs were also compared between the 2017 ACC/AHA and JNC 7 guidelines (traditional criteria) of hypertension. Results Compared to systolic blood pressure (SBP)<120 mm Hg, participants with SBP≥160 mm Hg had a hazard ratio (HR) of 1.50 (95% confidence interval (CI)=1.09, 2.06) for DMCs. Similarly, DMCs risk increased by 9% for every 10 mm Hg of higher SBP at baseline (95% CI=1.04, 1.13). The highest tercile SBP GRS was associated with 32% higher DMCs risk (95% CI=1.11, 1.56) compared to the lowest tercile. We found no significant differences in DMCs incidence between JNC 7 and 2017 ACC/AHA guidelines. Conclusions Genetic and epidemiological evidence suggests participants with higher SBP had an increased risk of DMCs, but hypertension defined by 2017 ACC/AHA guidelines may not impact DMCs incidence compared with JNC 7 criteria, contributing to the care and prevention of DMCs. [ABSTRACT FROM AUTHOR]

Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. Implications for research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system. [ABSTRACT FROM AUTHOR]

Wearable inertial measurement units (IMUs) are being used to quantify gait characteristics that are associated with increased fall risk, but the current limitation is the lack of contextual information that would clarify IMU data. Use of wearable video-based cameras would provide a comprehensive understanding of an individual's habitual fall risk, adding context to clarify abnormal IMU data. Generally, there is taboo when suggesting the use of wearable cameras to capture real-world video, clinical and patient apprehension due to ethical and privacy concerns. This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment. Specifically, no person sees the raw video data to understand context, rather AI interprets the raw video data first to blur sensitive objects and uphold privacy. That may be more routinely achieved than one imagines as contemporary resources exist. Here, to showcase/display the potential an exemplar model is suggested via off-the-shelf methods to detect and blur sensitive objects (e.g., people) with an accuracy of 88%. Here, the benefit of the proposed approach includes a more comprehensive understanding of an individual's free-living fall risk (from free-living IMU-based gait) without compromising privacy. More generally, the video and AI approach could be used beyond fall risk to better inform habitual experiences and challenges across a range of clinical cohorts. Medicine is becoming more receptive to wearables as a helpful toolbox, camera-based devices should be plausible instruments. [ABSTRACT FROM AUTHOR]

The UK government plans to limit price‐based and location‐based promotions for products high in saturated fat, salt and sugars. The 2004/2005 UK Nutrient Profiling Model (NPM) is the proposed legislative basis, but may be superseded by the draft 2018 NPM. This study develops an algorithm to apply both NPMs to a large food composition database (FCDB), and assesses implementation challenges. UK NPMs were applied algorithmically to the myfood24 FCDB, representing ~45 000 retail products. Pass rates – indicating free or restricted promotions – and micronutrient compositions were compared. Challenges were assessed, and recommendations addressed the legislation's public consultation questions. For products in scope (75% of total), 6% fewer passed the 2018 NPM (36%, P < 0.001) compared with the 2004/2005 NPM (42%). Beverages showed the greatest reduction in pass rate (75%). Under both models, micronutrient contents (per 100 g of product) were generally lower for products that passed; except folate, vitamin C and vitamin D were no different for passed and failed products. Compared with products passing the 2004/2005 NPM, products passing the 2018 NPM on average had marginally higher amounts of iron (0.05 mg, 95% CI: 0.02, 0.08, P < 0.001) and magnesium (1.00 mg, 95% CI: 0.00, 1.17, P = 0.029), but marginally lower levels of calcium (−0.42 mg, 95% CI: −2.00, −0.40, P = 0.025). Missing ingredient information and heterogeneous product categories were challenges for both NPMs. Free sugars calculation further complicated 2018 NPM application. To balance feasibility and public health benefit, the proposed legislative basis may not be appropriate. [ABSTRACT FROM AUTHOR]

Objectives To develop and validate a tool to predict the risk of an older adult experiencing medication- related harm (MRH) requiring healthcare use following hospital discharge. Design, setting, participants Multicentre, prospective cohort study recruiting older adults (≥65 years) discharged from five UK teaching hospitals between 2013 and 2015. Primary outcome measure Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non- adherence, harm from medication error). Three data sources provided MRH and healthcare use information: hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated. Results 1116 out of 1280 recruited participants completed follow- up (87%). Uncertain MRH cases ('possible' and 'probable') were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced 'definite' MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge: age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool's discrimination C- statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches. Conclusions The PRIME tool could be used to identify older patients at high risk of MRH requiring healthcare use following hospital discharge. Prior to clinical use we recommend the tool's evaluation in other settings. [ABSTRACT FROM AUTHOR]

The article focuses on a study which examines large-scale observational evidence of cross-cultural differences in facial behavior using advanced in computer science. Topics discussed include the automated facial coding used, the reliability of the facial action unit detection algorithms across different testing subsets, multilevel regression estimates predicting each facial action's base rate percentage.

Objective: To formally evaluate the written discharge advice for people with mild traumatic brain injury (mTBI). Methods: Eleven publications met the inclusion criteria: (1) intended for adults; (2) ≤two A4 pages; (3) published in English; (4) freely accessible; and (5) currently used (or suitable for use) in Australian hospital emergency departments or similar settings. Two independent raters evaluated the content and style of each publication against established standards. The readability of the publication, the diagnostic term(s) contained in it and a modified Patient Literature Usefulness Index (mPLUI) were also evaluated. Results: The mean content score was 19.18 ± 8.53 (maximum = 31) and the mean style score was 6.8 ± 1.34 (maximum = 8). The mean Flesch-Kincaid reading ease score was 66.42 ± 4.3. The mean mPLUI score was 65.86 ± 14.97 (maximum = 100). Higher scores on these metrics indicate more desirable properties. Over 80% of the publications used mixed diagnostic terminology. One publication scored optimally on two of the four metrics and highly on the others. Discussion: The content, style, readability and usefulness of written mTBI discharge advice was highly variable. The provision of written information to patients with mTBI is advised, but this variability in materials highlights the need for evaluation before distribution. Areas are identified to guide the improvement of written mTBI discharge advice. [ABSTRACT FROM AUTHOR]

Objective An international panel of pain specialists (anesthesiology, neurology, neurosurgery, and psychology) and research methodologists developed a screening tool to identify patients who may be suitable for spinal cord stimulation ( SCS)-the Refractory Chronic Pain Screening Tool ( RCPST) prototype. We describe a feasibility study to explore practicality and validity of this prototype. Design Consecutive outpatients were screened in two centers ( United Kingdom and United States). Sixty chronic pain adults without satisfactory pain relief despite treatment were assessed using RCPST (by pain specialist without expertise in neurostimulation) and then evaluated by two pain specialists experienced in SCS implantation and management to determine whether the patient should be referred for SCS. To maintain blinding, the participating physicians did not inform each other or the patient of assessment outcome. Sensitivity and specificity of the RCPST prototype were calculated using implanters' judgment as 'gold standard.' Results The average age of patients was 47.7 years; 53% were female. Fifty-seven patients completed the study (one withdrew consent, two lost to follow-up). The pain specialists agreed the prototype was easy to use and took <10 minutes to complete. Implanter agreement was moderate ( Kappa: 0.63, 95% confidence interval: 0.35-0.91). The prototype had low sensitivity (40%, 19-61%) and moderate specificity (78%, 65-92%). Using the same questionnaire with a modified decision algorithm, new prototypes were generated with range of high sensitivity (80-100%) and specificity (89-97%) values. Conclusions The RCPST aims to identify patients that should be referred for consideration for neurostimulation. The final implant decision requires appropriate neurological diagnostic workup, psychological assessment, and trial stimulation. RCPST was considered practical for routine clinical practice and contained appropriate questions. Sensitivity needs to be improved. A future study should select and validate the ideal RCPST prototype. [ABSTRACT FROM AUTHOR]

Background and purpose An internet-based discrete choice experiment (DCE) was conducted to elicit preferences for a wide range of Dupuytren's contracture (DC)-related health states. An algorithm was subsequently developed to convert these preferences into health state utilities that can be used to assess DC's impact on quality of life and the value of its treatments. Methods Health state preferences for varying levels of DC hand severity were elicited via an internet survey from a sample of the UK adult population. Severity levels were defined using a combination of contractures (0, 45, or 90 degrees) in 8 proximal interphalangeal and metacarpophalangeal joints of the index, middle, ring, and little fingers. Right-handed, left-handed, and ambidextrous respondents indicated which hand was preferable in each of the 10 randomly-selected hand-pairings comparing different DC severity levels. For consistency across comparisons, anatomically precise digital hand drawings were used. To anchor preferences onto the traditional 0-1 utility scale used in health economic evaluations, unaffected hands were assigned a utility of 1.0 whereas the utility for a maximally affected hand (i.e., all 8 joints set at 90 degrees of contracture) was derived by asking respondents to indicate what combination of attributes and levels of the EQ-5D-5L profile most accurately reflects the impact of living with such hand. Conditional logistic models were used to estimate indirect utilities, then rescaled to the anchor points on the EQ-5D-5L. Results Estimated utilities based on the responses of 1,745 qualified respondents were 0.49, 0.57, and 0.63 for completely affected dominant hands, non-dominant hands, or ambidextrous hands, respectively. Utility for a dominant hand with 90-degree contracture in t h e metacarpophalangeal joints of the ring and little fingers was estimated to be 0.89. Separately, reducing the contracture of metacarpophalangeal joint for a little finger from 50 to 12 degrees would improve utility by 0.02. Interpretation DC is associated with substantial utility decre- ments. The algorithms presented herein provide a robust and flexible framework to assess utility for varying degrees of DC severity. [ABSTRACT FROM AUTHOR]

BackgroundPredictD is a risk algorithm that was developed to predict risk of onset of major depression over 12 months in general practice attendees in Europe and validated in a similar population in Chile. It was the first risk algorithm to be developed in the field of mental disorders. Our objective was to extend predictD as an algorithm to detect people at risk of major depression over 24 months.MethodParticipants were 4190 adult attendees to general practices in the UK, Spain, Slovenia and Portugal, who were not depressed at baseline and were followed up for 24 months. The original predictD risk algorithm for onset of DSM-IV major depression had already been developed in data arising from the first 12 months of follow-up. In this analysis we fitted predictD to the longer period of follow-up, first by examining only the second year (12–24 months) and then the whole period of follow-up (0–24 months).ResultsThe instrument performed well for prediction of major depression from 12 to 24 months [c-index 0.728, 95% confidence interval (CI) 0.675–0.781], or over the whole 24 months (c-index 0.783, 95% CI 0.757–0.809).ConclusionsThe predictD risk algorithm for major depression is accurate over 24 months, extending it current use of prediction over 12 months. This strengthens its use in prevention efforts in general medical settings. [ABSTRACT FROM AUTHOR]

Purpose: To test if two of the adverse event triggers proposed by the Institute of Healthcare Improvement can detect adverse drug events (ADEs) in a UK secondary care setting, using an electronic prescribing and health record system. Methods: In order to identify triggers for over-anticoagulation and potential opioid overdose and we undertook a retrospective review of electronic medical and prescription records from 54,244 hospital admissions over a 1-year period, alongside a review of medical incident reports. Once prescription data were linked to triggers and duplicates were removed, case note review eliminated the false positive ADEs. Additionally, we tested the use of an electronic algorithm for the International Normalized Ratio (INR) ≥6 trigger. Results: The INR ≥6 electronic trigger identified 46 potential ADEs and the naloxone electronic trigger identified 82 ADEs. Based on the available case note review, the INR ≥6 trigger had a positive predictive value (PPV) of 38 % (14/37) and the naloxone trigger had a PPV of 91 % (61/67). The electronic algorithm for the INR ≥6 trigger identified 12 ADEs, thus reducing the need of case note review. This was in comparison with one and two critical incidents reported in the trust medical incident reports system, which respectively related to over-coagulation with warfarin and over-sedation with opioid medication. Conclusions: We have integrated automated and manual methods of detecting ADEs using previously defined triggers. Incorporating electronic triggers in already established electronic health records with prescription and laboratory test data can improve the detection of ADEs, and potentially lead to methods to avert them. [ABSTRACT FROM AUTHOR]

Background: Short-term exposure to ozone has been associated with increased daily mortality. The shape of the concentration-response relationship-and, in particular, if there is a threshold-is critical for estimating public health impacts. Objective: We investigated the concentration-response relationship between daily ozone and mortality in five urban and five rural areas in the United Kingdom from 1993 to 2006. Methods: We used Poisson regression, controlling for seasonality, temperature, and influenza, to investigate associations between daily maximum 8-hr ozone and daily all-cause mortality, assuming linear, linear-threshold, and spline models for all-year and season-specific periods. We examined sensitivity to adjustment for particles (urban areas only) and alternative temperature metrics. Results: In all-year analyses, we found clear evidence for a threshold in the concentration-response relationship between ozone and all-cause mortality in London at 65 µg/m3 [95% confidence interval (CI): 58, 83] but little evidence of a threshold in other urban or rural areas. Combined linear effect estimates for all-cause mortality were comparable for urban and rural areas: 0.48% (95% CI: 0.35, 0.60) and 0.58% (95% CI: 0.36, 0.81) per 10-µg/m3 increase in ozone concentrations, respectively. Seasonal analyses suggested thresholds in both urban and rural areas for effects of ozone during summer months. Conclusions: Our results suggest that health impacts should be estimated across the whole ambient range of ozone using both threshold and nonthreshold models, and models stratified by season. Evidence of a threshold effect in London but not in other study areas requires further investigation. The public health impacts of exposure to ozone in rural areas should not be overlooked. [ABSTRACT FROM AUTHOR]

Background: UK medical schools typically have over 300 students per year, making it impossible for students to know all the others well. Aims: This longitudinal cohort study measured the formation of medical student social networks and their relationship to grades. Method: In November 2009, 215/317 (68%) Year 2 UCL medical students reported their friendships with others in their year, by questionnaire. Multiple regression assessed the relationship between friendships, exam results and background variables (obtained from student records), with permutation testing to assess statistical significance. Results: Students of the same sex, the same ethnic group, and in the same tutor and small groups (to which they were randomly assigned at the start of medical school) were socially closer. Taking into account absolute difference in Year 1 grades, Year 2 pairs who were socially closer in November 2009 had more similar May 2010 grades. Individual student variables did not predict similarity in 2010 grades after taking friendships into account. Conclusions: The results suggest that medical students chose friends of the same sex and ethnic group as themselves; but random allocation of students to tutor groups also influenced friendships. Most importantly, friendships related to subsequent exam performance, suggesting friendship may influence learning. [ABSTRACT FROM AUTHOR]

Background Evidence suggests that in decision contexts characterized by uncertainty and time constraints (e.g. health-care decisions), fast and frugal decision-making strategies (heuristics) may perform better than complex rules of reasoning. Objective To examine whether it is possible to design deliberation components in decision support interventions using simple models (fast and frugal heuristics). Design The 'Take The Best' heuristic (i.e. selection of a 'most important reason') and 'The Tallying' integration algorithm (i.e. unitary weighing of pros and cons) were used to develop two deliberation components embedded in a Web-based decision support intervention for women facing amniocentesis testing. Ten researchers (recruited from 15), nine health-care providers (recruited from 28) and ten pregnant women (recruited from 14) who had recently been offered amniocentesis testing appraised evolving versions of 'your most important reason' (Take The Best) and 'weighing it up' (Tallying). Results Most researchers found the tools useful in facilitating decision making although emphasized the need for simple instructions and clear layouts. Health-care providers however expressed concerns regarding the usability and clarity of the tools. By contrast, 7 out of 10 pregnant women found the tools useful in weighing up the pros and cons of each option, helpful in structuring and clarifying their thoughts and visualizing their decision efforts. Several pregnant women felt that 'weighing it up' and 'your most important reason' were not appropriate when facing such a difficult and emotional decision. Conclusion Theoretical approaches based on fast and frugal heuristics can be used to develop deliberation tools that provide helpful support to patients facing real-world decisions about amniocentesis. [ABSTRACT FROM AUTHOR]

BackgroundThere are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.MethodFamily practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.ResultsThere were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.ConclusionsPredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression. [ABSTRACT FROM PUBLISHER]