http://bura.brunel.ac.uk/handle/2438/8631 2026-07-04T08:43:49Z http://bura.brunel.ac.uk/handle/2438/33550 Title: Learning in Teams, Thinking Analytically: A TBL Approach to Simulation Education Authors: Kashefi, A; Alwzinani, F 2025-07-01T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33549 Title: LLM4SCREENLIT: Recommendations on assessing the performance of large language models for screening literature in systematic reviews Authors: Madeyski, L; Kitchenham, B; Shepperd, M Abstract: Context: Large language models (LLMs) are increasingly used to screen literature for systematic reviews (SRs), but the standard confusion-matrix metrics used to evaluate them can mislead under the imbalanced, cost-asymmetric conditions of screening. Objective: We develop and justify LLM4SCREENLIT — practical recommendations for researchers conducting LLM-screening evaluations and for editors and reviewers assessing such studies — differentiated by study type (retrospective benchmarking vs. deployment for a specific SR). Method: Using Delgado-Chaves et al. (2025), an 18-LLM benchmark across three biomedical SRs, as a motivating example, we reviewed 28 additional papers and extracted their reported metrics. We propose a Weighted Matthews Correlation Coefficient (WMCC) that integrates MCC’s chance-correction with asymmetric misclassification costs, and validated it on three software-engineering (SE) reanalyses (Felizardo et al. 2024; Syriani et al. 2024; Huotala et al. 2025), the largest covering 9 LLMs 24 SE secondary studies (34,528 articles). Results: Across the 29 papers, only 10% reported MCC, only 24% reported full confusion matrices, and none of the five papers claiming workload savings priced false-negative cost. In the largest SE reanalysis, MCC and WMCC disagree on the best LLM in 55% of evaluable studies; in the most striking 9695-article SE study, the Accuracy-best LLM loses 63.3% of relevant evidence (Lost Evidence), the MCC-best 43.9%, but the WMCC-best only 5.8%. Sensitivity analysis (median crossover at , all ) supports as a conservative default. Conclusions: SR-screening evaluations should prioritise Lost Evidence and use cost-sensitive WMCC alongside MCC for ranking. Reporting must include the full confusion matrix and treat unclassifiable outputs as positives requiring human review. Designs should be leakage-aware, with non-LLM baselines when the study aims to inform SR practice and labels are available. Editors and reviewers should require these elements as routine. Extension to full-text screening and data extraction is principled but pending empirical validation. 2026-06-08T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33523 Title: Ontology-based Keyword Enriched Topic Modelling for Biomedical Research Trends: A Business Intelligence Perspective Authors: Altarawneh, A; Tucker, A; Swift, S; Arzoky, M Abstract: Over the past ten years, the biomedical literature has increased exponentially, and it is becoming increasingly cumbersome for the researchers and policymakers to identify emerging directions in research and allocate resources appropriately. Bibliometric indicators that are traditional and topic models based on keywords have weaknesses of synonym ambiguity, lag times and shallow semantics. The paper presents a new pipeline, which adds hierarchical concepts of the Medical Subject Headings (MeSH) ontology to author keywords and then uses Latent Dirichlet Allocation (LDA) to identify topics. A data set of 624,555 journal articles from 2015 to 2024 has been collected from the Scopus API in medicine, health professions and molecular biology. Four different systematic representations, raw keywords, MeSH parent categories, direct MeSH matches, and fully enriched keywords are pre-processed. The results showed that using fully enriched keywords with MeSH ontology annotations significantly enhanced topic coherence from 0.338 (baseline keywords) to 0.574, producing more interpretable and semantically consistent topics. The spatial analysis demonstrates another evidence of changing research priorities at the geographical level. The suggested framework illustrates the strength of ontology-driven modelling and visual analytics to assist in making business-intelligence decisions in rapidly evolving biomedical settings. 2026-05-12T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33522 Title: Ontology-Enriched Abstract Topic Modelling for Biomedical Trend Discovery: A MeSH-based LDA Framework with BI Visual Analytics Authors: Altarawneh, A; Swift, S; Tucker, A; Arzoky, M Abstract: The explosive growth of biomedical literature increasingly makes it difficult to identify incipient topics and long-term developments. Citation-based indicators are slow to capture developments and often fail to represent influence. Whilst Latent Dirichlet Allocation (LDA) supports content-based trend discovery, its bag-of-words assumption can overlook synonymy, polysemy, and hierarchical relations that are central to biomedical terminology. Moreover, recent work provides limited direct evidence on how far MeSH-enriched abstract representations improve LDA-based trend analysis when compared against the original abstract text. To fill this gap, an ontology-driven methodology for the pre-topic modelling of article abstracts by enriching them with hierarchical Medical Subject Headings (MeSH) terms is introduced in this study. We evaluate four representations, namely original abstracts, MeSH parent categories, MeSH-matched terms with parents, and a fully enriched representation, using a decade-long Scopus corpus of 624,486 biomedical journal articles (2015–2024). Model performance is assessed using coherence, distinctiveness, perplexity, and stability. The fully enriched representation achieves the strongest overall results (coherence 0.606, distinctiveness 0.996, stability 0.725, perplexity 1782.040) relative to the original abstract baseline. Temporal topic analysis further reveals interpretable patterns, including the COVID-19 surge and longer-term structural shifts such as the rise of machine-learning diagnostics. These findings indicate that MeSH-guided abstract enrichment can strengthen topic quality and support more effective biomedical trend discovery through business-intelligence visual analytics. 2026-05-12T00:00:00Z