The effect of static scanning and mobility training on mobility in people with hemianopia after stroke: A randomized controlled trial comparing standardized versus non-standardized treatment protocols

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Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

Factors influencing scar formation following Bacille Calmette-Guérin (BCG) vaccination

The prevalence of scar formation following Bacille Calmette-Guérin (BCG) vaccination varies globally. The beneficial off-target effects of BCG are proposed to be stronger amongst children who develop a BCG scar. Within an international randomised trial ('BCG vaccination to reduce the impact of coronavirus disease 2019 (COVID-19) in healthcare workers'; BRACE Trial), this nested prospective cohort study assessed the prevalence of and factors influencing scar formation, as well as participant perception of BCG scarring 12 months following vaccination . Amongst 3071 BCG-recipients, 2341 (76%) developed a BCG scar. Scar prevalence was lowest in Spain and highest in UK. Absence of post-injection wheal (OR 0.4, 95%CI 0.2-0.9), BCG revaccination (OR 1.7, 95%CI 1.3-2.0), female sex (OR 2.0, 95%CI 1.7-2.4), older age (OR 0.4, 95%CI 0.4-0.5) and study country (Brazil OR 1.6, 95%CI 1.3-2.0) influenced BCG scar prevalence. Of the 2341 participants with a BCG scar, 1806 (77%) did not mind having the scar. Participants more likely to not mind were those in Brazil, males and those with a prior BCG vaccination history. The majority (96%) did not regret having the vaccine. Both vaccination-related (amenable to optimisation) and individual-related factors affected BCG scar prevalence 12 months following BCG vaccination of adults, with implications for maximising the effectiveness of BCG vaccination.Paola Villanueva, Nigel W. Crawford, Mariana Garcia Croda, Simone Collopy, Bruno Araújo Jardim, Tyane de Almeida Pinto Jardim, Laurens Manning, Michaela Lucas, Helen Marshall, Cristina Prat-Aymerich, Alice Sawka, Ketaki Sharma, Darren Troeman, Ushma Wadia, Adilia Warris, Nicholas Wood, Nicole L. Messina, Nigel Curtis, Laure F. Pitte

A study protocol of a randomised controlled trial incorporating a health economic analysis to investigate if additional allied health services for rehabilitation reduce length of stay without compromising patient outcomes

Background Reducing patient length of stay is a high priority for health service providers. Preliminary information suggests additional Saturday rehabilitation services could reduce the time a patient stays in hospital by three days. This large trial will examine if providing additional physiotherapy and occupational therapy services on a Saturday reduces health care costs, and improves the health of hospital inpatients receiving rehabilitation compared to the usual Monday to Friday service. We will also investigate the cost effectiveness and patient outcomes of such a service. Methods/Design A randomised controlled trial will evaluate the effect of providing additional physiotherapy and occupational therapy for rehabilitation. Seven hundred and twelve patients receiving inpatient rehabilitation at two metropolitan sites will be randomly allocated to the intervention group or control group. The control group will receive usual care physiotherapy and occupational therapy from Monday to Friday while the intervention group will receive the same amount of rehabilitation as the control group Monday to Friday plus a full physiotherapy and occupational therapy service on Saturday. The primary outcomes will be patient length of stay, quality of life (EuroQol questionnaire), the Functional Independence Measure (FIM), and health utilization and cost data. Secondary outcomes will assess clinical outcomes relevant to the goals of therapy: the 10 metre walk test, the timed up and go test, the Personal Care Participation Assessment and Resource Tool (PC PART), and the modified motor assessment scale. Blinded assessors will assess outcomes at admission and discharge, and follow up data on quality of life, function and health care costs will be collected at 6 and 12 months after discharge. Between group differences will be analysed with analysis of covariance using baseline measures as the covariate. A health economic analysis will be carried out alongside the randomised controlled trial. Discussion This paper outlines the study protocol for the first fully powered randomised controlled trial incorporating a health economic analysis to establish if additional Saturday allied health services for rehabilitation inpatients reduces length of stay without compromising discharge outcomes. If successful, this trial will have substantial health benefits for the patients and for organizations delivering rehabilitation services

Golden Rule of Forecasting: Be Conservative

This article proposes a unifying theory, or the Golden Rule, or forecasting. The Golden Rule of Forecasting is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek out and use all knowledge relevant to the problem, including knowledge of methods validated for the situation. Twenty-eight guidelines are logically deduced from the Golden Rule. A review of evidence identified 105 papers with experimental comparisons; 102 support the guidelines. Ignoring a single guideline increased forecast error by more than two-fifths on average. Ignoring the Golden Rule is likely to harm accuracy most when the situation is uncertain and complex, and when bias is likely. Non-experts who use the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data, have led forecasters to violate the Golden Rule. As a result, despite major advances in evidence-based forecasting methods, forecasting practice in many fields has failed to improve over the past half-century

Selecting Forecasting Methods

I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods

An effectiveness analysis of healthcare systems using a systems theoretic approach

<p>Abstract</p> <p>Background</p> <p>The use of accreditation and quality measurement and reporting to improve healthcare quality and patient safety has been widespread across many countries. A review of the literature reveals no association between the accreditation system and the quality measurement and reporting systems, even when hospital compliance with these systems is satisfactory. Improvement of health care outcomes needs to be based on an appreciation of the whole system that contributes to those outcomes. The research literature currently lacks an appropriate analysis and is fragmented among activities. This paper aims to propose an integrated research model of these two systems and to demonstrate the usefulness of the resulting model for strategic research planning.</p> <p>Methods/design</p> <p>To achieve these aims, a systematic integration of the healthcare accreditation and quality measurement/reporting systems is structured hierarchically. A holistic systems relationship model of the administration segment is developed to act as an investigation framework. A literature-based empirical study is used to validate the proposed relationships derived from the model. Australian experiences are used as evidence for the system effectiveness analysis and design base for an adaptive-control study proposal to show the usefulness of the system model for guiding strategic research.</p> <p>Results</p> <p>Three basic relationships were revealed and validated from the research literature. The systemic weaknesses of the accreditation system and quality measurement/reporting system from a system flow perspective were examined. The approach provides a system thinking structure to assist the design of quality improvement strategies. The proposed model discovers a fourth implicit relationship, a feedback between quality performance reporting components and choice of accreditation components that is likely to play an important role in health care outcomes. An example involving accreditation surveyors is developed that provides a systematic search for improving the impact of accreditation on quality of care and hence on the accreditation/performance correlation.</p> <p>Conclusion</p> <p>There is clear value in developing a theoretical systems approach to achieving quality in health care. The introduction of the systematic surveyor-based search for improvements creates an adaptive-control system to optimize health care quality. It is hoped that these outcomes will stimulate further research in the development of strategic planning using systems theoretic approach for the improvement of quality in health care.</p

Supporting Science Teacher Learning: The Role of Educative Curriculum Materials

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43135/1/10972_2004_Article_5095704.pd