J Biomed Inform. 2021 Aug 25:103897. doi: 10.1016/j.jbi.2021.103897. Online ahead of print.

ABSTRACT

INTRODUCTION: Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post-hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, data is often collected and stored via electronic Case Report Forms (eCRFs) in Electronic Data Capture (EDC) systems. By implementing a de-novo FAIRification process in such a system, the reusability and, thus, scalability of FAIRification across research projects can be greatly improved. In this study, we developed and implemented a novel method for de-novo FAIRification via an EDC system. We evaluated our method by applying it to the Registry of Vascular Anomalies (VASCA).

METHODS: Our EDC and research project independent method ensures that eCRF data entered into an EDC system can be transformed into machine-readable, FAIR data using a semantic data model (a canonical representation of the data, based on ontology concepts and semantic web standards) and mappings from the model to questions on the eCRF. The FAIRified data are stored in a triple store and can, together with associated metadata, be accessed and queried through a FAIR Data Point. The method was implemented in Castor EDC, an EDC system, through a data transformation application. The FAIRness of the output of the method, the FAIRified data and metadata, was evaluated using the FAIR Evaluation Services.

RESULTS: We successfully applied our FAIRification method to the VASCA registry. Data entered on eCRFs is automatically transformed into machine-readable data and can be accessed and queried using SPARQL queries in the FAIR Data Point. Twenty-one FAIR Evaluator tests pass and one test regarding the metadata persistence policy fails, since this policy is not in place yet.

CONCLUSION: In this study, we developed a novel method for de-novo FAIRification via an EDC system. Its application in the VASCA registry and the automated FAIR evaluation show that the method can be used to make clinical research data FAIR when they are entered in an eCRF without any intervention from data management and data entry personnel. Due to the generic approach and developed tooling, we believe that our method can be used in other registries and clinical trials as well.

PMID:34454078 | DOI:10.1016/j.jbi.2021.103897

Sensors (Basel). 2021 Aug 12;21(16):5435. doi: 10.3390/s21165435.

ABSTRACT

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system's feasibility, where the participants show a positive interest in its functionalities.

PMID:34450877 | DOI:10.3390/s21165435

PLoS One. 2021 Aug 26;16(8):e0256705. doi: 10.1371/journal.pone.0256705. eCollection 2021.

ABSTRACT

The huge amount of data made available by the massive usage of social media has opened up the unprecedented possibility to carry out a data-driven study of political processes. While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked. With the present paper, we carry out a socio-semantic analysis of the debate about migration policies observed on the Italian Twittersphere, across the period May-November 2019. As regards the social analysis, our methodology allows us to extract relevant information about the political orientation of the communities of users-hereby called partisan communities-without resorting upon any external information. Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces. As regards the semantic analysis, our networks of hashtags display a mesoscale structure organized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment.

PMID:34437640 | PMC:PMC8389375 | DOI:10.1371/journal.pone.0256705

BMC Bioinformatics. 2021 Aug 25;22(Suppl 9):105. doi: 10.1186/s12859-021-04031-9.

ABSTRACT

BACKGROUND: Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration.

RESULTS: We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language.

CONCLUSION: We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.

PMID:34433410 | DOI:10.1186/s12859-021-04031-9

PeerJ Comput Sci. 2021 Aug 3;7:e649. doi: 10.7717/peerj-cs.649. eCollection 2021.

ABSTRACT

Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model.

PMID:34435097 | PMC:PMC8356649 | DOI:10.7717/peerj-cs.649

MethodsX. 2021 Jul 23;8:101460. doi: 10.1016/j.mex.2021.101460. eCollection 2021.

ABSTRACT

Despite the intense research activity in the last two decades, ontology integration still presents a number of challenging issues. As ontologies are continuously growing in number, complexity and size and are adopted within open distributed systems such as the Semantic Web, integration becomes a central problem and has to be addressed in a context of increasing scale and heterogeneity. In this paper, we describe a holistic alignment-based method for customized ontology integration. The holistic approach proposes additional challenges as multiple ontologies are jointly integrated at once, in contrast to most common approaches that perform an incremental pairwise ontology integration. By applying consolidated techniques for ontology matching, we investigate the impact on the resulting ontology. The proposed method takes multiple ontologies as well as pairwise alignments and returns a refactored/non-refactored integrated ontology that faithfully preserves the original knowledge of the input ontologies and alignments. We have tested the method on large biomedical ontologies from the LargeBio OAEI track. Results show effectiveness, and overall, a decreased integration cost over multiple ontologies.•OIAR and AROM are two implementations of the proposed method.•OIAR creates a bridge ontology, and AROM creates a fully merged ontology.•The implementation includes the option of ontology refactoring.

PMID:34434866 | PMC:PMC8374672 | DOI:10.1016/j.mex.2021.101460

Front Psychol. 2021 Aug 6;12:697550. doi: 10.3389/fpsyg.2021.697550. eCollection 2021.

ABSTRACT

Remote data collection procedures can strengthen developmental science by addressing current limitations to in-person data collection and helping recruit more diverse and larger samples of participants. Thus, remote data collection opens an opportunity for more equitable and more replicable developmental science. However, it remains an open question whether remote data collection procedures with children participants produce results comparable to those obtained using in-person data collection. This knowledge is critical to integrate results across studies using different data collection procedures. We developed novel web-based versions of two tasks that have been used in prior work with 4-6-year-old children and recruited children who were participating in a virtual enrichment program. We report the first successful remote replication of two key experimental effects that speak to the emergence of structured semantic representations (N = 52) and their role in inferential reasoning (N = 40). We discuss the implications of these findings for using remote data collection with children participants, for maintaining research collaborations with community settings, and for strengthening methodological practices in developmental science.

PMID:34421748 | PMC:PMC8377201 | DOI:10.3389/fpsyg.2021.697550

Artif Intell Med. 2021 Aug;118:102131. doi: 10.1016/j.artmed.2021.102131. Epub 2021 Jun 25.

ABSTRACT

Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from >400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.

PMID:34412847 | DOI:10.1016/j.artmed.2021.102131

J Biomed Semantics. 2021 Aug 9;12(1):15. doi: 10.1186/s13326-021-00246-0.

ABSTRACT

BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to limitations in the logic or the subject domain semantics. Examples are dealing with different foundational ontologies in ontology alignment and OWL 2 DL's transitive object property versus a qualified cardinality constraint. Such conflicts have to be resolved somehow. However, only isolated and fragmented guidance for doing so is available, which therefore results in ad hoc decision-making that may not be the best choice or forgotten about later.

RESULTS: This work aims to address this by taking steps towards a framework to deal with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. It proposes an initial library of common conflicts, a conflict set, typical steps toward resolution, and the software availability and requirements needed for it. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak, being avian influenza, and running examples with COVID-19 ontologies.

CONCLUSIONS: The evaluation demonstrated the potential and feasibility of a conflict resolution framework for ontologies.

PMID:34372934 | PMC:PMC8352153 | DOI:10.1186/s13326-021-00246-0

BMJ Open. 2021 Jul 23;11(7):e053402. doi: 10.1136/bmjopen-2021-053402.

ABSTRACT

OBJECTIVE: To examine inequalities in COVID-19 vaccination rates among elderly adults in England.

DESIGN: Cohort study.

SETTING: People living in private households and communal establishments in England.

PARTICIPANTS: 6 655 672 adults aged ≥70 years (mean 78.8 years, 55.2% women) who were alive on 15 March 2021.

MAIN OUTCOME MEASURES: Having received the first dose of a vaccine against COVID-19 by 15 March 2021. We calculated vaccination rates and estimated unadjusted and adjusted ORs using logistic regression models.

RESULTS: By 15 March 2021, 93.2% of people living in England aged 70 years and over had received at least one dose of a COVID-19 vaccine. While vaccination rates differed across all factors considered apart from sex, the greatest disparities were seen between ethnic and religious groups. The lowest rates were in people of black African and black Caribbean ethnic backgrounds, where only 67.2% and 73.8% had received a vaccine, with adjusted odds of not being vaccinated at 5.01 (95% CI 4.86 to 5.16) and 4.85 (4.75 to 4.96) times greater than the white British group. The proportion of individuals self-identifying as Muslim and Buddhist who had received a vaccine was 79.1% and 84.1%, respectively. Older age, greater area deprivation, less advantaged socioeconomic position (proxied by living in a rented home), being disabled and living either alone or in a multigenerational household were also associated with higher odds of not having received the vaccine.

CONCLUSION: Research is now urgently needed to understand why disparities exist in these groups and how they can best be addressed through public health policy and community engagement.

PMID:34301672 | PMC:PMC8313303 | DOI:10.1136/bmjopen-2021-053402

JMIR Med Inform. 2021 Jul 22;9(7):e30115. doi: 10.2196/30115.

ABSTRACT

BACKGROUND: Medical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children's health promotion and education.

OBJECTIVE: Using authoritative and highly influential web-based children's health educational resources from the Nemours Foundation, the largest not-for-profit organization promoting children's health and well-being, we aimed to develop machine learning algorithms to discriminate and predict the writing styles of health educational resources on children versus adult health promotion using a variety of health educational resources aimed at the general public.

METHODS: The selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using ridge classifier, support vector machine, extreme gradient boost tree, and recursive feature elimination followed by revision by education experts. We compared algorithms using the automatically selected (n=19) and linguistically enhanced (n=20) feature sets, using the initial feature set (n=115) as the baseline.

RESULTS: Using five-fold cross-validation, compared with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P=.02; 95% CI -0.016 to 0.1929), mean specificity (P=.02; 95% CI -0.016 to 0.199), mean area under the receiver operating characteristic curve (P=.02; 95% CI -0.007 to 0.140), and mean macro F1 (P=.006; 95% CI 0.016-0.167). The statistically improved performance of the final model (20 features) is in contrast to the statistically insignificant changes between the original feature set (n=115) and the automatically selected features (n=19): mean sensitivity (P=.13; 95% CI -0.1699 to 0.0681), mean specificity (P=.10; 95% CI -0.1389 to 0.4017), mean area under the receiver operating characteristic curve (P=.008; 95% CI 0.0059-0.1126), and mean macro F1 (P=.98; 95% CI -0.0555 to 0.0548). This demonstrates the importance and effectiveness of combining automatic feature selection and expert-based linguistic revision to develop the most effective machine learning algorithms from high-dimensional data sets.

CONCLUSIONS: We developed new evaluation tools for the discrimination and prediction of writing styles of web-based health resources for children's health education and promotion among parents and caregivers of children. User-adaptive automatic assessment of web-based health content holds great promise for distant and remote health education among young readers. Our study leveraged the precision and adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.

PMID:34292167 | DOI:10.2196/30115

Sci Data. 2021 Jul 12;8(1):169. doi: 10.1038/s41597-021-00962-3.

ABSTRACT

The EPA developed the Adverse Outcome Pathway Database (AOP-DB) to better characterize adverse outcomes of toxicological interest that are relevant to human health and the environment. Here we present the most recent version of the EPA Adverse Outcome Pathway Database (AOP-DB), version 2. AOP-DB v.2 introduces several substantial updates, which include automated data pulls from the AOP-Wiki 2.0, the integration of tissue-gene network data, and human AOP-gene data by population, semantic mapping and SPARQL endpoint creation, in addition to the presentation of the first publicly available AOP-DB web user interface. Potential users of the data may investigate specific molecular targets of an AOP, the relation of those gene/protein targets to other AOPs, cross-species, pathway, or disease-AOP relationships, or frequencies of AOP-related functional variants in particular populations, for example. Version updates described herein help inform new testable hypotheses about the etiology and mechanisms underlying adverse outcomes of environmental and toxicological concern.

PMID:34253739 | DOI:10.1038/s41597-021-00962-3

Front Physiol. 2021 Jun 24;12:693735. doi: 10.3389/fphys.2021.693735. eCollection 2021.

ABSTRACT

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps, and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.

PMID:34248680 | PMC:PMC8265045 | DOI:10.3389/fphys.2021.693735

PeerJ Comput Sci. 2021 Jun 18;7:e558. doi: 10.7717/peerj-cs.558. eCollection 2021.

ABSTRACT

Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products' comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects' synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure.

PMID:34239969 | PMC:PMC8237320 | DOI:10.7717/peerj-cs.558

Saudi J Biol Sci. 2021 Jul;28(7):3886-3892. doi: 10.1016/j.sjbs.2021.03.068. Epub 2021 Apr 2.

NO ABSTRACT

PMID:34220244 | PMC:PMC8241633 | DOI:10.1016/j.sjbs.2021.03.068

Int J Environ Res Public Health. 2021 Jun 28;18(13):6915. doi: 10.3390/ijerph18136915.

ABSTRACT

BACKGROUND: Many countries around the world are currently threatened by the COVID-19 pandemic, and nurses are facing increasing responsibilities and work demands related to infection control. To establish a developmental strategy for infection control, it is important to analyze, understand, or visualize the accumulated data gathered from research in the field of nursing.

METHODS: A total of 4854 articles published between 1978 and 2017 were retrieved from the Web of Science. Abstracts from these articles were extracted, and network analysis was conducted using the semantic network module.

RESULTS: 'wound', 'injury', 'breast', "dressing", 'temperature', 'drainage', 'diabetes', 'abscess', and 'cleaning' were identified as the keywords with high values of degree centrality, betweenness centrality, and closeness centrality; hence, they were determined to be influential in the network. The major topics were 'PLWH' (people living with HIV), 'pregnancy', and 'STI' (sexually transmitted infection).

CONCLUSIONS: Diverse infection research has been conducted on the topics of blood-borne infections, sexually transmitted infections, respiratory infections, urinary tract infections, and bacterial infections. STIs (including HIV), pregnancy, and bacterial infections have been the focus of particularly intense research by nursing researchers. More research on viral infections, urinary tract infections, immune topic, and hospital-acquired infections will be needed.

PMID:34203191 | DOI:10.3390/ijerph18136915

Bioinformatics. 2021 Jun 23:btab461. doi: 10.1093/bioinformatics/btab461. Online ahead of print.

ABSTRACT

MOTIVATION: Substance abuse constitutes one of the major contemporary health epidemics. Recently, the use of social media platforms has garnered interest as a novel source of data for drug addiction epidemiology. Often however, the language used in such forums comprises slang and jargon. Currently, there are no publicly available resources to automatically analyse the esoteric language-use in the social media drug-use sub-culture. This lacunae introduces critical challenges for interpreting, sensemaking and modeling of addiction epidemiology using social media.

RESULTS: Drug-Use Insights (DUI) is a public and open-source web application to address the aforementioned deficiency. DUI is underlined by a hierarchical taxonomy encompassing 108 different addiction related categories consisting of over 9,000 terms, where each category encompasses a set of semantically related terms. These categories and terms were established by utilizing thematic analysis in conjunction with term embeddings generated from 7,472,545 Reddit posts made by 1,402,017 redditors. Given post(s) from social media forums such as Reddit and Twitter, DUI can be used foremost to identify constituent terms related to drug use. Furthermore, the DUI categories and integrated visualization tools can be leveraged for semantic- and exploratory analysis. To the best of our knowledge, DUI utilizes the largest number of substance use and recovery social media posts used in a study and represents the first significant online taxonomy of drug abuse terminology.

AVAILABILITY: The DUI web server and source code are available at: http://haddock9.sfsu.edu/insight/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34164647 | DOI:10.1093/bioinformatics/btab461

Biomed Res Int. 2021 May 28;2021:9923112. doi: 10.1155/2021/9923112. eCollection 2021.

ABSTRACT

Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods were developed to deal with this challenge, few considered semantic relationship between residues. We combined long short-term memory (LSTM) and convolutional neural network (CNN) into a deep learning method for predicting succinylation site. The proposed method obtained a Matthews correlation coefficient of 0.2508 on the independent test, outperforming state of the art methods. We also performed the enrichment analysis of succinylation proteins. The results showed that functions of succinylation were conserved across species but differed to a certain extent with species. On basis of the proposed method, we developed a user-friendly web server for predicting succinylation sites.

PMID:34159204 | PMC:PMC8188601 | DOI:10.1155/2021/9923112

Proc Conf. 2021 Jun;2021:106-115.

ABSTRACT

Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io.

PMID:34151319 | PMC:PMC8212692

Int J Lang Commun Disord. 2021 Jun 16. doi: 10.1111/1460-6984.12644. Online ahead of print.

ABSTRACT

BACKGROUND: Prenatal alcohol exposure (PAE) is associated with growth deficits and neurodevelopmental impairment including foetal alcohol spectrum disorder (FASD). Difficulties with oral and written communication skills are common among children with PAE; however, less is known about how communication skills of adolescents who have PAE compare with those who do not. Adolescence is a critical time for development, supporting the transition into adulthood, but it is considered a high-risk period for those with FASD.

AIMS: We conducted a systematic review to synthesize evidence regarding oral and written communication skills of adolescents with PAE or FASD and how they compare with those with no PAE.

METHODS & PROCEDURES: A comprehensive search strategy used seven databases: Cochrane Library, Cinahl, Embase, Medline, PsycInfo, Eric and Web of Science. Included studies reported on at least one outcome related to oral and written communication for a PAE (or FASD) group as well as a no/low PAE group, both with age ranges of 10-24 years. Quality assessment was undertaken.

MAIN CONTRIBUTION: Communication skills most often assessed in the seven studies included in this review were semantic knowledge, semantic processing, and verbal learning and memory. These communication skills, in addition to reading and spelling, were commonly weaker among adolescents with PAE compared with those with no/low PAE. However, the findings were inconsistent across studies, and studies differed in their methodologies.

CONCLUSIONS & IMPLICATIONS: Our results emphasize that for adolescents with PAE, communication skills in both oral and written modalities should be comprehensively understood in assessment and when planning interventions. A key limitation of the existing literature is that comparison groups often include some participants with a low level of PAE, and that PAE definitions used to allocate participants to groups differ across studies.

WHAT THIS PAPER ADDS: What is already known on the subject PAE and FASD are associated with deficits in oral and written communication skills. Studies to date have mostly focused on children with a FASD diagnosis as well as combined groups of children and adolescents with FASD or PAE. There is a gap in what is known about oral and written communication skills of adolescents, specifically, who have PAE or FASD. This has implications for the provision of assessment and supports during a period of increased social and academic demands. What this study adds to existing knowledge This review provides systematic identification, assessment and synthesis of the current literature related to oral and written communication skills of adolescents with PAE compared with those with no/low PAE. The review revealed a small knowledge base with inconsistent methodologies and findings across studies. However, the findings overall highlight that adolescents with PAE have weaker skills in oral and written language than those with no/low PAE. Results are discussed in relation to education, social and emotional well-being, and forensic contexts. What are the potential or actual clinical implications of this work? Findings emphasize that for adolescents with PAE, comprehensive assessment of both oral and written communication skills, through both standardized and functional tasks, should be undertaken. Speech-language pathologists have a key role in assessment with individuals who have PAE.

PMID:34137136 | DOI:10.1111/1460-6984.12644