Is a one-size-fits-all ‘12-month-rule' appropriate when it comes to the last search date in systematic reviews?

The problem: Searches conducted a year or more before submission of a systematic review (SR) paper can result in journal editors or peer-reviewers rejecting it. Their concerns are that findings from SRs with ‘out-of-date’ searches might provide decision-makers with misleading evidence.1 Although recent technological advances have helped to speed up some review processes,2 other methodological advances have increased the work required such that reviews often require longer than 12 months to produce useful and rigorous findings. This puts many SRs at risk of rejection by journal editors. We argue that a blanket 12-month cut-off point for searches is not appropriate, that it may hinder the dissemination of important research, and may have a knock-on impact on reviewers’ willingness to undertake the most ambitious reviews. We also argue that not all SRs are equally at risk of being ‘out of date’ at 12 months; while intervention effectiveness reviews in fast-moving areas may become outdated well before 12 months,3 others, such as qualitative evidence syntheses, are unlikely to have their findings substantially changed by the inclusion of new evidence. We focus on recent developments in SR designs, methods and technologies, to reflect on whether existing journal publishing guidelines are at odds with current SR approaches designed to improve review quality and usefulness

Intervention Component Analysis (ICA): a pragmatic approach for identifying the critical features of complex interventions

Background: In order to enable replication of effective complex interventions, systematic reviews need to provide evidence about their critical features and clear procedural details for their implementation. Currently, few systematic reviews provide sufficient guidance of this sort. / Methods: Through a worked example, this paper reports on a methodological approach, Intervention Component Analysis (ICA), specifically developed to bridge the gap between evidence of effectiveness and practical implementation of interventions. By (a) using an inductive approach to explore the nature of intervention features and (b) making use of trialists’ informally reported experience-based evidence, the approach is designed to overcome the deficiencies of poor reporting which often hinders knowledge translation work whilst also avoiding the need to invest significant amounts of time and resources in following up details with authors. / Results: A key strength of the approach is its ability to reveal hidden or overlooked intervention features and barriers and facilitators only identified in practical application of interventions. It is thus especially useful where hypothesised mechanisms in an existing programme theory have failed. A further benefit of the approach is its ability to identify potentially new configurations of components that have not yet been evaluated. / Conclusions: ICA is a formal and rigorous yet relatively streamlined approach to identify key intervention content and implementation processes. ICA addresses a critical need for knowledge translation around complex interventions to support policy decisions and evidence implementation

Factors influencing the delivery of telerehabilitation for stroke: a systematic review

ObjectiveDespite the available evidence regarding effectiveness of stroke telerehabilitation, there has been little focus on factors influencing its delivery or translation from the research setting into practice. There are complex challenges to embedding telerehabilitation into stroke services and generating transferable knowledge about scaling up and routinising this service model. This review aimed to explore factors influencing the delivery of stroke telerehabilitation interventions, including platforms, technical requirements, training, support, access, cost, usability and acceptability.MethodsMEDLINE, EMBASE, CINAHL, Web of Science and Cochrane Library and Central Registry of Clinical Trials were searched to identify full-text articles of randomised controlled trials (RCTs) and protocols for RCTs published since a Cochrane review on stroke telerehabilitation services. A narrative synthesis was conducted, providing a comprehensive description of the factors influencing stroke telerehabilitation intervention delivery.ResultsThirty-one studies and ten protocols of ongoing studies were included. Interventions were categorised as synchronous telerehabilitation (n = 9), asynchronous telerehabilitation (n = 11) and tele-support (n = 11). Telephone and videoconference were the most frequently used modes of delivery. Usability and acceptability with telerehabilitation were high across all platforms, although access issues and technical challenges may be potential barriers to the use of telerehabilitation in service delivery. Costs of intervention delivery and training requirements were poorly reported.ConclusionsThis review synthesises the evidence relating to factors that may influence stroke telerehabilitation intervention delivery at a crucial timepoint given the rapid deployment of telerehabilitation in response to the COVID-19 pandemic. It recommends strategies, such as ensuring adequate training and technical infrastructure, shared learning and consistent reporting of cost and usability and acceptability outcomes, to overcome challenges in embedding and routinising this service model and priorities for research in this area

Use of technology to prevent, detect, manage and control hypertension in sub-Saharan Africa : a systematic review

Objective: To identify and assess the use of technologies, including mobile health technology, internet of things (IoT) devices and artificial intelligence (AI) in hypertension healthcare in sub-Saharan Africa (SSA). Design: Systematic review. Data sources: Medline, Embase, Scopus and Web of Science. Eligibility criteria: Studies addressing outcomes related to the use of technologies for hypertension healthcare (all points in the healthcare cascade) in SSA. Methods: Databases were searched from inception to 2 August 2021. Screening, data extraction and risk of bias assessment were done in duplicate. Data were extracted on study design, setting, technology(s) employed and outcomes. Blood pressure (BP) reduction due to intervention was extracted from a subset of randomised controlled trials. Methodological quality was assessed using the Mixed Methods Appraisal Tool. Results: 1717 hits were retrieved, 1206 deduplicated studies were screened and 67 full texts were assessed for eligibility. 22 studies were included, all reported on clinical investigations. Two studies were observational, and 20 evaluated technology-based interventions. Outcomes included BP reduction/control, treatment adherence, retention in care, awareness/knowledge of hypertension and completeness of medical records. All studies used mobile technology, three linked with IoT devices. Short Message Service (SMS) was the most popular method of targeting patients (n=6). Moderate BP reduction was achieved in three randomised controlled trials. Patients and healthcare providers reported positive perceptions towards the technologies. No studies using AI were identified. Conclusions: There are a range of successful applications of key enabling technologies in SSA, including BP reduction, increased health knowledge and treatment adherence following targeted mobile technology interventions. There is evidence to support use of mobile technology for hypertension management in SSA. However, current application of technologies is highly heterogeneous and key barriers exist, limiting efficacy and uptake in SSA. More research is needed, addressing objective measures such as BP reduction in robust randomised studies. PROSPERO registration number: CRD42020223043

Clearing the fog : a scoping literature review on the ethical issues surrounding artificial intelligence-based medical devices

The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public

A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings

Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are common in low- and middle-income countries (LMICs). Accurate referrals can reduce the devastating impact of pneumonia, especially in LRS. Discriminating pneumonia from other respiratory conditions based only on symptoms is a major challenge. Machine learning has shown promise in overcoming the diagnostic difficulties of pneumonia (i.e., low specificity of symptoms, lack of accessible diagnostic tests and varied clinical presentation). Many scientific papers are now focusing on deep-learning methods applied to clinical images, which is unaffordable for initial patient referral in LMICs. The current study used a dataset of 4500 patients (1500 patients affected by bronchitis, 3000 by pneumonia) from a middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable in LRS and in community settings. Three common machine learning methods were tested and compared: logistic regression; decision tree and support vector machine. Models were developed through a holdout process of training-validation and testing. We focused on six clinically relevant, easily interpreted patient symptoms as best indicators for pneumonia. Our final model was a decision tree, achieving an AUC of 93%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of pneumonia in LRS and in community settings

Oral contraceptive use in Premiership and Championship women's rugby union: perceived symptomology, management strategies, and performance and wellness effects

The aim of this study was to investigate the prevalence of oral contraceptive use in domestic rugby union, to compare symptomology by contraceptive use, and to determine symptom management strategies. Additionally, to characterise the perceived influence of oral contraceptive use and non-use on wellness and performance. A total of 238 Premiership and Championship women's rugby union players completed an online questionnaire. The survey was comprised of questions relating to player characteristics, hormonal or non-hormonal contraceptive characteristics, perceived symptomology, symptom management strategies, and performance and wellness characteristics. The prevalence of oral contraceptive users was 26%. Non-hormonal contraceptive users reported greater perceived negative symptomology (i.e., back pain, nausea, sore breasts) and performance and wellness effects (i.e., fatigue, stress, mood, concentration, power, match-play) than oral contraceptive users. The most common symptom management strategies were medication (33%), nutritional interventions (20%), and training modulation (20%). Twelve percent of players had previously spoken to staff about their menstrual cycle (i.e., regular and irregular) or contraceptive use. The most common barriers to speaking to staff were 'male staff' (29%) and 'club culture' (24%). The importance of assisting non-hormonal contraceptive users in managing symptoms is evident. Emphasis on overcoming barriers to staff-player dialogue regarding menstrual/contraceptive cycle is required

The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms : a systematic review

Artificial Intelligence (AI) systems using symptoms/signs to detect respiratory diseases may improve diagnosis especially in limited resource settings. Heterogeneity in such AI systems creates an ongoing need to analyse performance to inform future research. This systematic literature review aimed to investigate performance and reporting of diagnostic AI systems using machine learning (ML) for pneumonia detection based on symptoms and signs, and to provide recommendations on best practices for designing and implementing predictive ML algorithms. This article was conducted following the PRISMA protocol, 876 articles were identified by searching PubMed, Scopus, and OvidSP databases (last search 5th May 2021). For inclusion, studies must have differentiated clinically diagnosed pneumonia from controls or other diseases using AI. Risk of Bias was evaluated using The STARD 2015 tool. Information was extracted from 16 included studies regarding study characteristics, ML-model features, reference tests, study population, accuracy measures and ethical aspects. All included studies were highly heterogenous concerning the study design, setting of diagnosis, study population and ML algorithm. Study reporting quality in methodology and results was low. Ethical issues surrounding design and implementation of the AI algorithms were not well explored. Although no single performance measure was used in all studies, most reported an accuracy measure over 90%. There is strong evidence to support further investigations of ML to automatically detect pneumonia based on easily recognisable symptoms and signs. To help improve the efficacy of future research, recommendations for designing and implementing AI tools based on the findings of this study are provided

Methanol Worked Examples for the TEA and LCA Guidelines for CO2 Utilization

This document contains worked examples of how to apply the accompanying “Guideline for Techno-Economic Assessment of CO2 Utilization” and “Guideline for Life Cycle Assessment of CO2 Utilization”. The Guidelines can be downloaded via http://hdl.handle.net/2027.42/145436. These worked examples are not intended to be a definitive TEA or LCA report on the process described, but are provided as supporting material to show how the TEA and LCA methodologies described in the guidelines can be specifically applied to tackle the issues surrounding CO2 utilization. This document describes techno-economic assessment and life cycle assessment for methanol production. As methanol production via hydrogenation and PEM electrolysis of water to produce hydrogen are both at high technology readiness levels (TRL7+); a CO2 capture technology currently at a lower TRL (membrane separation at TRL3 or 4) was selected to demonstrate the differences that can be observed in the interpretation phase when working on TEA and LCA studies of processes with lower TRLs. It is acknowledged that there are many unknown variables with membrane capture, and it is not within the remit of this work to draw conclusions on their application. However, it is known that organizations wish to conduct TEA and LCA studies across a range of TRLs and therefore we hope to demonstrate here how this could affect the results. This document is one of several application examples that accompany the parent document “Techno-Economic Assessment & Life-Cycle Assessment Guidelines for CO2 Utilization”.Development of standardized CO2 Life Cycle and Techno-economic Assessment Guidelines was commissioned by CO2 Sciences, Inc., with the support of 3M, EIT Climate-KIC, CO2 Value Europe, Emissions Reduction Alberta, Grantham Foundation for the Protection of the Environment, R. K. Mellon Foundation, Cynthia and George Mitchell Foundation, National Institute of Clean and Low Carbon Energy, Praxair, Inc., XPRIZE and generous individuals who are committed to action to address climate change.https://deepblue.lib.umich.edu/bitstream/2027.42/145723/5/Global CO2 Initiative Complete Methanol Study 2018.pd