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Creating high-quality dictionary entries has been a meticulous and time-consuming process, but current developments in Artificial Intelligence (AI) could help to speed up this process. This study explores the use of ChatGPT to create COBUILD-style English monolingual entries and evaluates its ability to generate accurate and high-quality sense definitions and example sentences on a sample of fifteen verbs of communication. AI-generated entries are produced using ChatGPT Plus, and human experts evaluate them alongside COBUILD entries in a blinded randomized process. The quality of sense definitions, example sentences, and overall entries are assessed using a five-point scale. The results indicate that AI-generated definitions are comparable to those created by human lexicographers, while AI-generated examples and overall entries receive lower ratings. The study also explores the possibility of fine-tuning AI models based on expert feedback to enhance their performance.

This paper examines the implications of AI and machine translation on traditional lexicography, using three canonical scenarios for dictionary use: text reception, text production, and text translation as test cases. With the advent of high-capacity, AI-driven language models such as OpenAI’s GPT-3 and GPT-4, and the efficacy of machine translation, the utility of conventional dictionaries comes under question. Despite these advancements, the study finds that lexicography remains relevant, especially for less-documented languages where AI falls short, but human lexicographers excel in data-sparse environments. It argues for the importance of lexicography in promoting linguistic diversity and maintaining the integrity of lesser-known languages. Moreover, as AI technologies progress, they present opportunities for lexicographers to expand their methodology and embrace interdisciplinarity. The role of lexicographers is likely to shift towards guiding and refining increasingly automated tools, ensuring ethical linguistic data use, and counteracting AI biases.

Patients 65 or older with hypertension who switched from hydrochlorothiazide to chlorthalidone did not have fewer major cardiovascular events or non–cancer-related deaths than those who continued receiving hydrochlorothiazide.

In a 48-week trial in patients with active rheumatoid arthritis despite treatment with methotrexate, adding sulfasalazine and hydroxychloroquine to methotrexate was not inferior to adding etanercept. The prognosis for patients with rheumatoid arthritis has improved dramatically over the past two decades. 1 , 2 The reasons for the improved prognosis include earlier diagnosis, treatment targeted to low disease activity or remission, the use of disease-modifying antirheumatic drugs (DMARDs) in combinations, and the availability of biologic therapies. 1 – 4 A substantial portion of patients who are diagnosed today will have a clinical remission with therapy. 1 , 2 , 5 , 6 Unfortunately, the cost of treating rheumatoid arthritis has also risen dramatically, and this disease is now more expensive to treat than diabetes, 7 largely as a consequence of the biologic therapies. Most clinicians . . .

The Department of Lexicography and Lexicology at Adam Mickiewicz University in Poznań has done extensive research on dictionary use in the context of language teaching and learning. The department forms part of the Faculty of English, which is the largest institution in continental Europe educating teachers of English at the B.A., M.A., and Ph.D. levels. Therefore, it is only natural that the language-teaching potential of dictionaries is the main focus of the studies undertaken in the Department.

The study explores an approach to supplementing existing CEFR -graded vocabulary lists, which are often incomplete, by imputing CEFR levels for additional vocabulary items. This is achieved by analysing word-level data such as dictionary views, corpus frequency, part-of-speech, and polysemy. Using English as a test case, the study employs a variety of machine-learning models to predict CEFR levels for words not included in the initial set. The models significantly outperform a random baseline, indicating their effectiveness. The findings suggest that corpus frequency is the most influential predictor, followed by dictionary views and polysemy. The study reveals the potential of this semi-automatic approach to expand CEFR -graded word lists, making them more comprehensive and accessible for language learners. At the same time, human oversight is recommended to ensure the appropriateness of the imputed words for language learners, such as regarding the inclusion of potentially offensive terms. Future research may extend this methodology to other languages, provided that sufficient linguistic data is available.

This contribution explores the relationship between the English CEFR (Common European Framework of Reference for Languages) vocabulary levels and user interest in English Wiktionary entries. User interest was operationalized through the number of views of these entries in Wikimedia server logs covering a period of four years (2019–2022). Our findings reveal a significant relationship between CEFR levels and user interest: entries classified at lower CEFR levels tend to attract more views, which suggests a greater user interest in more basic vocabulary. A multiple regression model controlling for other known or potential factors affecting interest: corpus frequency, polysemy, word prevalence, and age of acquisition confirmed that lower CEFR levels attract significantly more views even after taking into account the other predictors. These findings highlight the importance of CEFR levels in predicting which words users are likely to look up, with implications for lexicography and the development of language learning materials.

This study aims to establish what lexical factors make it more likely for dictionary users to consult specific articles in a dictionary using the English Wiktionary log files, which include records of user visits over the course of 6 years. Recent findings suggest that lexical frequency is a significant factor predicting look-up behavior, with the more frequent words being more likely to be consulted. Three further lexical factors are brought into focus: (1) age of acquisition; (2) lexical prevalence; and (3) degree of polysemy operationalized as the number of dictionary senses. Age of acquisition and lexical prevalence data were obtained from recent published studies and linked to the list of visited Wiktionary lemmas, whereas polysemy status was derived from Wiktionary entries themselves. Regression modeling confirms the significance of corpus frequency in explaining user interest in looking up words in the dictionary. However, the remaining three factors also make a contribution whose nature is discussed and interpreted. Knowing what makes dictionary users look up words is both theoretically interesting and practically useful to lexicographers, telling them which lexical items should be prioritized in lexicographic work. Plain Language Summary What makes people look up words in the English Wiktionary? This study aims to establish what factors make it more likely for dictionary users to consult specific articles in a dictionary using the English Wiktionary log files, which include records of user visits over the course of six years. Recent findings suggest that word frequency is a significant factor predicting look-up behaviour, with the more frequent words being more likely to be consulted. Three further factors are brought into focus: (1) age of acquisition, which is the age at which a word is learned; (2) lexical prevalence, which is how many people know the word; and (3) degree of polysemy calculated as the number of dictionary senses. Age of acquisition and lexical prevalence data were obtained from recent published studies and linked to the list of visited Wiktionary lemmas, whereas polysemy status was derived from Wiktionary entries themselves. Our study confirms the significance of word frequency in explaining user interest in looking up words in the dictionary. However, the remaining three factors also make a contribution whose nature is discussed and interpreted. Knowing what makes dictionary users look up words is both theoretically interesting and practically useful to lexicographers, telling them which words should be prioritized in lexicographic work.

Traditionally, language learners have relied on dictionaries when trying to read or write in a foreign language. However, new LLM-based chatbots may offer an alternative to traditional dictionaries as lexical tools. This study assesses the effectiveness of ChatGPT versus the monolingual Longman Dictionary of Contemporary English (LDOCE, 2024 ) and the bilingual Diki.pl (Diki.pl, 2024 ) online dictionaries in supporting English language learners in receptive and productive lexical tasks. With a sample of 166 university students at B2 to C1 proficiency levels and forty uncommon English phrasal verbs, we investigate whether a leading AI-driven chatbot, a high-quality learners’ dictionary, or a popular free bilingual dictionary offers better support in accurately understanding and producing English. The results reveal ChatGPT to be more effective than either dictionary in production, and better than the monolingual dictionary, but not the bilingual dictionary, in reception.

Low-dose naltrexone (LDN) is commonly used to control pain and other symptoms, especially in patients with autoimmune diseases, but with limited evidence. This study tests the efficacy of LDN in reducing chronic pain in patients with osteoarthritis (OA) and inflammatory arthritis (IA), where existing approaches often fail to adequately control pain. In this randomized, double-blind, placebo-controlled, crossover clinical trial, each patient received 4.5 mg LDN for 8 weeks and placebo for 8 weeks. Outcome measures were patient reported, using validated questionnaires. The primary outcome was differences in pain interference during the LDN and placebo periods, using the Brief Pain Inventory (scale, 0–70). Secondary outcomes included changes in mean pain severity, fatigue, depression, and multiple domains of health-related quality of life. The painDETECT questionnaire classified pain as nociceptive, neuropathic, or mixed. Data were analyzed using mixed-effects models. Seventeen patients with OA and 6 with IA completed the pilot study. Most patients described their pain as nociceptive (n = 9) or mixed (n = 8) rather than neuropathic (n = 3). There was no difference in change in pain interference after treatment with LDN (mean [SD], −23 [19.4]) versus placebo (mean [SD], −22 [19.2]; P = 0.90). No significant differences were seen in pain severity, fatigue, depression, or health-related quality of life. In this small pilot study, findings do not support LDN being efficacious in reducing nociceptive pain due to arthritis. Too few patients were enrolled to rule out modest benefit or to assess inflammatory or neuropathic pain. ClinicalTrials.gov identifier: NCT03008590.