I wanted to know why this development of communication in English with Germans was taking place and if this was only a development particular to the Netherlands, or Limburg, or were other cultures also experiencing the same. However, yearning to know the answer is one thing, but having the opportunity to study this phenomenon is another.
Background & aimsThe Scored Patient-Generated Subjective Global Assessment (PG-SGA©) is a validated nutritional screening, assessment, monitoring, and triage tool. When translated to other languages, the questions and answering items need to be conceptually, semantically, and operationally equivalent to the original tool. In this study, we aimed to assess linguistic and content validity of the PG-SGA translated and culturally adapted for the Norwegian setting, as perceived by Norwegian cancer patients and healthcare professionals (HCPs).MethodsWe have translated and culturally adapted the original PG-SGA for the Norwegian setting, in concordance with the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Cancer patients and HCPs, including nurses, dietitians and physicians, were invited to participate. Comprehensibility and difficulty were assessed by patients for the patient component (PG-SGA Short Form), and by HCPs for the professional component. Content validity was assessed for the full PG-SGA by HCPs only. The data were collected by a questionnaire and evaluations were operationalized by a 4-point scale. Item and scale indices were calculated for comprehensibility (Item CI, Scale CI), difficulty (Item DI, Scale DI) and content validity (Item CVI, Scale CVI).ResultsFifty-one cancer patients and 92 HCPs participated in the study. The patients perceived comprehensibility and difficulty of the Norwegian PG-SGA Short Form as excellent (Scale CI = 0.99 and DI = 0.97). However, HCPs perceived comprehensibility and difficulty of the professional component as below acceptable (Scale CI = 0.78 and DI = 0.66), and the physical exam was being rated as the most difficult part (Item DI 0.26 to 0.65). Content validity for the full Norwegian PG-SGA was considered excellent (Scale CVI = 0.99) by the HCPs.ConclusionThe patient component of PG-SGA was considered clear and easy to complete, and the full Norwegian PG-SGA was considered as relevant by HCPs. In the final Norwegian PG-SGA, changes have been made to improve comprehensibility of the professional component. To improve perceived difficulty of completing the professional component, training of professionals is indicated.
To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
