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Background Using two large datasets from Dorset, we previously reported an internally validated multivariable risk model for predicting the risk of GI malignancy in IDA—the IDIOM score. The aim of this retrospective observational study was to validate the IDIOM model using two independent external datasets. Methods The external validation datasets were collected, in a secondary care setting, by different investigators from cohorts in Oxford and Sheffield derived under different circumstances, comprising 1117 and 474 patients with confirmed IDA respectively. The data were anonymised prior to analysis. The predictive performance of the original model was evaluated by estimating measures of calibration, discrimination and clinical utility using the validation datasets. Results The discrimination of the original model using the external validation data was 70% (95% CI 65, 75) for the Oxford dataset and 70% (95% CI 61, 79) for the Sheffield dataset. The analysis of mean, weak, flexible and across the risk groups’ calibration showed no tendency for under or over-estimated risks in the combined validation data. Decision curve analysis demonstrated the clinical value of the IDIOM model with a net benefit that is higher than ‘investigate all’ and ‘investigate no-one’ strategies up to a threshold of 18% in the combined validation data, using a risk cut-off of around 1.2% to categorise patients into the very low risk group showed that none of the patients stratified in this risk group proved to have GI cancer on investigation in the validation datasets. Conclusion This external validation exercise has shown promising results for the IDIOM model in predicting the risk of underlying GI malignancy in independent IDA datasets collected in different clinical settings.

To facilitate the clinical use of an algorithm for predicting the risk of gastrointestinal malignancy in iron deficiency anaemia—the IDIOM score, a software application has been developed, with a view to providing free and simple access to healthcare professionals in the UK. A detailed requirements analysis for intended users of the application revealed the need for an automated decision-support tool in which anonymised, individual patient data is entered and gastrointestinal cancer risk is calculated and displayed immediately, which lends itself to use in busy clinical settings. Human-centred design was employed to develop the solution, focusing on the users and their needs, whilst ensuring that they are provided with sufficient details to appropriately interpret the risk score. The IDIOM App has been developed using R Shiny as a web-based application enabling access from different platforms with updates that can be carried out centrally through the host server. The application has been evaluated through literature search, internal/external validation, code testing, risk analysis, and usability assessments. Legal notices, contact system with research and maintenance teams, and all the supportive information for the application such as description of the population and intended users have been embedded within the application interface. With the purpose of providing a guide of developing standalone software medical devices in academic setting, this paper aims to present the theoretical and practical aspects of developing, writing technical documentation, and certifying standalone software medical devices using the case of the IDIOM App as an example.

This paper presents IdiomsTube (https://www.idiomstube.com), the first-ever computer-assisted language learning tool for facilitating the learning of English idiomatic expressions from YouTube videos. The web-based tool is designed to automatically (1) assess the speech rate and lexical difficulty level of any learner-chosen English-captioned YouTube video; (2) generate a range of vocabulary-building and revision tasks (e.g. fill-in-the-blanks, the classic spelling game hangman, pronunciation, flashcards); and (3) recommend YouTube videos based on learners’ individual learning progress, class teachers’ instructions, and current trends. This paper describes the app’s current features in conjunction with the challenges endemic in the development of applications that automatically generate English language learning content out of internet video content. At the time of writing, the IdiomsTube app had over 8,000 registered users worldwide.

Researchers are more concerned now about the kind of information and consistency online dictionaries provide. This paper aims to study the treatment of English idioms in smartphone applications of English-Arabic dictionaries. The sample of this paper consists of five smartphone applications of English-Arabic dictionaries in the App Store. Eighteen English idioms were sampled based on their commonness in the English language. Oxford Wordpower Dictionary (English-English-Arabic) and Oxford English-Arabic Online Dictionary were used as references to compare the kind of information provided under each idiom. The data were analyzed both quantitatively and qualitatively. The study revealed that, out of five English-Arabic dictionary applications, none of them provided results for all the sampled English idioms despite their commonness. The qualitative analysis revealed that the majority of the sampled dictionary apps provide information, such as pronunciation and definition in Arabic and English. The study concluded with highlighting helpful features in these apps which conventional dictionaries lack, such as interactive voice search, history, favorites and image search.

This study explored how four groups of language learners used a mobile software application for learning idiomatic expressions and collocations. A total of 45 participants in the study used the application for a period of one week. Data collected from the application, a questionnaire, and follow-up interviews showed that participants usage of the application correlated with their average scores on the quizzes in the application and that usage of the application can be predicted by a variety of factors, such as language proficiency, users' average daily usage of their mobile devices, and motivation and learning goals. Findings also underscored the importance of providing language learners with resources to help them learn idiomatic expressions and collocation. Furthermore, results showed that participants have strong positive attitudes toward the use of mobile technology in language learning.

This activity uses an app to teach language skills. Ten-Story Building is a handheld app, currently for Android devices, that teaches students phrasal verbs, which are idiomatic expressions that are particularly troublesome for English as a second language students. The app uses a game-like mechanic and correlates to informal learning tools that focus on engagement, iteration, convenience, and high-interest content.