Objective:Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications.Methods:We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address the small size of the annotated corpus, we experimented with data augmentation approaches, including Easy Data Augmentation (EDA) and Prompt-Based Data Augmentation (PromDA). We applied the best-performing model to a set of about 12K RCT publications to characterize self-acknowledged limitations at larger scale.Results:Our data model consists of 15 categories and 24 sub-categories (e.g., Population and its sub-category DiagnosticCriteria). We annotated 1090 instances of limitation types in 952 sentences (4.8 limitation sentences and 5.5 limitation types per article). A fine-tuned PubMedBERT model for limitation sentence classification improved upon our earlier model by about 1.5 absolute percentage points in F1 score (0.821 vs. 0.8) with statistical significance (). Our best-performing limitation type classification model, PubMedBERT fine-tuning with PromDA (Output View), achieved an F1 score of 0.7, improving upon the vanilla PubMedBERT model by 2.7 percentage points, with statistical significance ().Conclusion:The model could support automated screening tools which can be used by journals to draw the authors’ attention to reporting issues. Automatic extraction of limitations from RCT publications could benefit peer review and evidence synthesis, and support advanced methods to search and aggregate the evidence from the clinical trial literature.
MULTIFILE
The aim of this small explorative study was to get an impression of the participants’ views and understanding of the role of becoming a teacher in Swedish schools, realising the characteristic of pedagogy aimed for in the curriculum (in Lgr11 and Lgy), specifically the interaction patterns and student participation in learning processes. Main research questions addressed participants expectations of differences and challenges in the Swedish school context as compared to their experiences in Syria contexts, in specific the development of their understanding of student participation in interaction as characteristic of Swedish education and curriculum. From this, recommendations are formulated for curriculum and research for future Fast Track trajectories.
As the first order of business in the RIGHT project, each region produced and published its own regional report, using an underlying format developed in work package 3 in this project (Manickam & van Lieshout, 2018). The format and the regional work consisted of three parts. Part 1 is the Regional Innovation Ecosystems (RIE) mapping to provide a qualitative understanding of the region’s innovation ecosystem with regards to its Smart Specialisation Strategies (S3). This part is divided into a socio-economic and R&D profile mapping and a SWOT analysis. The RIE is an adaptation of a methodology and tool used by the eDIGIREGION Project. This part is to be filled in by desk research and consulting regional experts (through interviews and/or focus groups). This part is used for mapping the own regional ecosystems, information for the partners to get to know the other regions and to be able to identify relevant similarities and differences across the regions, which in turn, will be reported in part 1 of this trans-regional report. Regions themselves chose their own sector focus. One could focus on either energy of the blue sector, or both. Part 2 focuses on the innovation capacity and needs of SMEs from the chosen sector(s). The questions are adapted from a systemic study on cluster developments, in which an analysis model was developed (Manickam, 2018). It is based on (on average) six face-to-face interviews with SMEs from the sector. The outputs of these interviews were summarised into one template, in English, by each partner region to allow for joint analysis and comparison that is in turn reported in part 2 of this report Part 3 introduced the Job Forecasting and Skills Gaps mapping using the JOES templates as developed by van Lieshout et al. (2017). To gain an appreciation of the extent and nature of skills gap, each region was asked to analyse current and potential future labour demand, workforce, and discrepancies between the two, in up to 2 businesses. For obvious reasons (confidentiality and privacy), the JOEs will not be published separately, nor will their information be used in the report in a way that would be traceable to specific businesses. We will use exemplary information from them for illustrative purposes in Parts 1 and 2 of this report where relevant.
LINK
