Effective Models of In-Class Remedial Reading Instruction

In light of No Child Left Behind, state assessments, and the pressure to serve children in the least restrictive environment, the methods used to meet student needs are changing considerably. Academic Intervention Services, Title I programs, and special education have all been impacted. This paper will discuss the role of a reading specialist today and how students can receive remedial reading instruction in the regular classroom setting. This topic was selected due to observations of the difficulty my school based literacy educator (SBLE) faces in trying to implement a push-in model of instruction. The purpose of this research is to help the school develop a repertoire of effective co-teaching strategies to ease the implementation of a push-in model. This research then will use previous studies in special education and co-teaching to develop strategies for reading instruction in the classroom. The previous work of Rita Bean dealing with effective reading specialists will be the backbone of the classroom observations that will take place throughout the study. At the end of the research the goal is to have identified the strategies that are already in use at the school and discover several effective collaborative strategies for the reading specialist and classroom teachers to begin to use more often to improve the current program. Ultimately the researcher hopes that these methods will continue to be utilized in the future, both in the school being used for the study and shared with other schools implementing the push-in model

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists. DESIGN: This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting. SETTING: The study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020. PARTICIPANTS: Eleven consultant diagnostic radiologists of varying levels of experience participated in this study. PRIMARY AND SECONDARY OUTCOME MEASURES: Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed. RESULTS: Of 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy. CONCLUSIONS: Use of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice

Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed

Citizen Science and Food: A Review

Citizen science and food is part of a new programme of work to explore how we can involve the communities we serve when building the evidence-base on which policy decisions are made. Citizen science is an approach that can provide high volumes of data with a wide geographic spread. It is relatively quick to deploy and allows access to evidence we would ordinarily have difficulty collating. This methodology has been endorsed by the European Commission for Research, Science and Innovation. There is no one size fits all definition, but citizen science projects involves engaging with communities and asking them to be part of the project, either through engaging them in data collection or through other ways of co-creation. For participants, citizen science offers learning opportunities, the satisfaction of contributing to scientific evidence and the potential to influence policy. It can also give us data which is high in volume, has wide geographical spread, is relatively quick to deploy and that we couldn’t access any other way. Projects using these methods often involve engaging with communities and asking them to be part of the project. This can be either through working with them in data collection, or through co-creation. This report demonstrates that the research community are already undertaking numerous pieces of research that align with FSA’s evidence needs. This includes examples from the UK and other global communities. Participants in such research have collected data on topics ranging from food preparation in the home to levels of chemical contaminant in foods. The findings of this report outline that citizen science could allow the FSA to target and facilitate more systematic engagement with UK and global research communities, to help address key research priorities of the FSA

Citizen science for the food system

The food system is hugely complex, encompassing many different actors, geographic areas and cultural contexts. Although the citizen science literature related to food and food systems is concentrated primarily on a few key areas of this complex system (i.e. on health and food production); citizen science has the potential to help address many grand challenges related to food and agriculture. In this chapter we make use of multiple desk-based reviews of the literature, and draw on our own experiences of citizen science projects. We provide examples of existing citizen science projects in the UK (as well as global initiatives) that can be adapted for use to help address food policy areas of research interest. We conclude that making use of citizen science approaches in food policy reseaarch can help the transition toward a more equitable and sustainable food and agriculture system

Engaging citizens in sustainability research: Comparing survey recruitment and responses between Facebook, Twitter and Qualtrics

Purpose The study aims to compare survey recruitment rates between Facebook, Twitter and Qualtrics and to assess the impact of recruitment method on estimates of energy content, food safety, carbon footprint and animal welfare across 29 foods. Design/methodology/approach Two versions of an online survey were developed on the citizen science platform, Zooniverse. The surveys explored citizen estimations of energy density (kcal) or carbon footprint (Co2) and food safety or animal welfare of 29 commonly eaten foods. Survey recruitment was conducted via paid promotions on Twitter and Facebook and via paid respondent invites on Qualtrics. The study included approximately 500 participants (Facebook, N˜11 (ratings 358), Twitter, N˜85 (ratings 2,184), Qualtrics, N = 398 (ratings 11,910)). Kruskal–Wallis and Chi-square analyses compared citizen estimations with validated values and assessed the impact of the variables on estimations. Findings Citizens were unable to accurately estimate carbon footprint and energy content, with most citizens overestimating values. Citizen estimates were most accurate for meat products. Qualtrics was the most successful recruitment method for the online survey. Citizen estimates between platforms were significantly different, suggesting that Facebook and Twitter may not be suitable recruitment methods for citizen online surveys. Practical implications Qualtrics was the favourable platform for survey recruitment. However, estimates across all recruitment platforms were poor. As paid recruitment methods such as Qualtrics are costly, the authors recommend continued examination of the social media environment to develop appropriate, affordable and timely online recruitment strategies for citizen science. Originality/value The findings indicate that citizens are unable to accurately estimate the carbon footprint and energy content of foods suggesting a focus on consumer education is needed to enable consumers to move towards more sustainable and healthy diets. Essential if we are to meet the 2030 Sustainable Development Goals of zero hunger, good health and wellbeing and responsible consumption and production. The study highlights the utility of Zooniverse for assessing citizen estimates of carbon footprint, energy content, animal welfare and safety of foods

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography

ObjectivesTo evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset.DesignA retrospective case–control study was undertaken.SettingCommunity radiology clinics and hospitals in Australia and the USA.ParticipantsA test dataset of 2557 chest radiography studies was ground-truthed by three subspecialty thoracic radiologists for the presence of simple or tension pneumothorax as well as each subgroup other than positioning. Radiograph positioning was derived from radiographer annotations on the images.Outcome measuresDCNN performance for detecting simple and tension pneumothorax was evaluated over the entire test set, as well as within each subgroup, using the area under the receiver operating characteristic curve (AUC). A difference in AUC of more than 0.05 was considered clinically significant.ResultsWhen compared with the overall test set, performance of the DCNN for detecting simple and tension pneumothorax was statistically non-inferior in all subgroups. The DCNN had an AUC of 0.981 (0.976–0.986) for detecting simple pneumothorax and 0.997 (0.995–0.999) for detecting tension pneumothorax.ConclusionsHidden stratification has significant implications for potential failures of deep learning when applied in clinical practice. This study demonstrated that a comprehensively trained DCNN can be resilient to hidden stratification in several clinically meaningful subgroups in detecting pneumothorax.</jats:sec

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June-August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.Jane Scheetz, Philip Rothschild, Myra McGuinness, Xavier Hadoux, H. Peter Soyer, Monika Janda, James J.J. Condon, Luke Oakden‑Rayner, Lyle J. Palmer, Stuart Keel, Peter van Wijngaarde

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines

The value of standards for health datasets in artificial intelligence-based applications

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative)