Medical Texts Research Articles

Autoimmune diseases and adverse pregnancy outcomes: an umbrella review

BackgroundThe prevalence of autoimmune conditions is two-fold higher in women than in men, especially during the reproductive years. Autoimmune conditions have been associated with a greater risk of adverse pregnancy outcomes, and some conditions have been studied more than others with inconsistent findings. The objective of this umbrella review was to identify, appraise, synthesise, and consolidate findings from published systematic reviews of autoimmune conditions and adverse pregnancy outcomes. MethodsIn this umbrella review, we searched Medline, Embase, and Cochrane databases for systematic reviews from inception to Sept 30, 2022, without language restrictions. We used the Medical Subject Headings and free text search for autoimmune conditions and pregnancy outcomes. Screening, data extraction, and quality appraisal (AMSTAR 2) were done by two independent reviewers. Data was extracted using a standardised form, which was piloted before use. Data were synthesised narratively and quantitatively. Odds ratios (ORs) with 95% CIs were reported. The protocol has been registered to PROSPERO (CRD42022334992). FindingsWe selected 33 reviews, which included 709 primary studies. Pregnant women with autoimmune conditions were at high risk of both adverse maternal and fetal outcomes. The risk of miscarriage was increased in pregnant women with Sjögren's syndrome (relative risk [RR] 8·85, 95% CI 3·10–25·26), systemic lupus erythematosus (SLE; OR 4·90, 95% CI 3·10–7·69), thyroid autoimmunity (OR 2·77, 2·10–3·65), systemic sclerosis (OR 1·60, 1·29–2·22), and coeliac disease (OR 1·38, 1·12–1·69). The risk of pre-eclampsia was increased in pregnant women with type 1 diabetes (T1DM; OR 4·19, 3·08–5·71) and SLE (OR 3·20, 2·54 – 4·20). The risk of gestational diabetes was increased in pregnant women with inflammatory bowel disease (IBD; OR 2·96, 1·47–5·98) and thyroid autoimmunity (OR 1·49, 1·07–2·07). The risk of intrauterine growth restriction (IUGR) was increased in pregnant women with systemic sclerosis (OR 3·20, 2·21–4·53) and coeliac disease (OR 1·71, 1·36–2·14). The risk of delivering a small-for-gestational age baby was increased in pregnant women with SLE (OR 2·49, 1·88–3·31) and rheumatoid arthritis (OR 1·49, 1·22–1·82). The risks of other fetal outcomes such as stillbirth, preterm birth, and low birthweight were also increased in pregnant women with autoimmune disorders. T1DM in women was associated with lower odds of small-for-gestational-age outcome (OR 0·68, 0·56–0·83). InterpretationPregnant women with autoimmune conditions are at greater risk of developing adverse pregnancy outcomes. Further research is required to develop better preconception to post-natal care for women with autoimmune conditions. FundingMedical Research Council (MRC) and the National Institute for Health and Care Research (NIHR).

Obesity from a sign of being rich to a disease of the new age: A historical review.

Obesity has historically been seen as a sign of wealth and social privilege, as can be inferred from studying the ancient works. We aimed to report the causes, diagnostic approaches, and treatment among the authentic books of traditional Iranian medicine. We searched the original versions of historical books and used a narrative approach to report the history of obesity. Obesity was often seen as an indicator of complete health. Obesity in healthy women was a requirement for beauty, based on descriptions of women from the Zoroaster period and from antiquity to the late Qajar period. This point of view existed during various ages. However, after the constitutional period, the view of obesity changed into that of an illness, due to modern ideas and offshore role models, especially during the Pahlavi era. This change led to serious attempts to treat obesity. Obesity is a critical problem that needs immediate attention to prevent substantial health consequences. Different medical paradigms have presented their criteria and foundations throughout history. The emphasis of Iranian alternative medicine was on prevention and the maintenance of health, with the next step being treatment. Prevention, treatment, consuming medicinal plants, and recovery have often been written about in the traditional books of medicine. Throughout the traditional Iranian medical texts, physicians have made recommendations about maintaining an appropriate body weight. The best treatment was prevention and a healthy lifestyle. The treatments for controlling and restricting obesity included paying attention to nourishment, mobility, and even the habitat.

Investigating the Rate of the Use of Persian Traditional Medicine Services in Tehran.

Despite all the advancements and publicity made in regard to classical medicine, every day more and more people are interested in complementary medicine. This study was designed and conducted to determine the relative frequency of the use of Persian traditional medicine services by the people of Tehran. This cross-sectional study was conducted using the telephone survey method in Tehran. A total of 1824 samples were included in the study based on Cochran's formula. At first, by searching databases, including Google Scholar, PubMed, Embase, and internal sources, including the Iran Medex database, numerous medical texts and articles were reviewed in the field of using traditional medicine services. Then, interview guide questions were designed and asked over the phone. Finally, the data were extracted and subjected to quantitative analysis. Frequency and percentage of relative frequency were used to describe the study data. In the study, 62% (n = 1131) of the participants were women, and 60.5% (n = 1103) of the participants have turned to modern medicine for treatment and have also used traditional medical treatments. Also, 43.5% (n = 864) of the participants have used herbal medicines and their products; 43% (n = 616) of the participants have used Persian traditional medicine treatments as self-treatment based on their personal information; and only 46.5% (n = 666) have evaluated the therapeutic effect of Persian traditional medicine methods as "good." The results of the study indicate the need for more emphasis on Persian traditional medicine alongside modern medicine, more organization and supervision of traditional medicine service providers in the country, and the creation of coherent and integrated management in this field.

The earliest English culinary recipes: dietary advice in Old English medical texts

ABSTRACT The earliest culinary recipes written in the English language, or in England, are contained in the three main Old English medical collections, now known as Bald’s Leechbook, Leechbook III and the Lacnunga, dating from the tenth, or possibly late ninth, to eleventh centuries. These recipes reveal contemporary ideas about the suitability of various foods for people experiencing various conditions, but not for a generalised ‘healthy diet’, nor indeed for the diet of the population at large in England at the time. They form part of a fairly substantial body of dietary advice, drawing quite heavily on a Latin tradition that was common to most of Western Europe, which betrays clear traces of humoral theory.

Shared Reading as a Community of Practice for Overcoming the Generation Gap and Improving Psychological Safety in Rural Family Medicine Education: A Grounded-Theory Approach.

Background A community of practice (CoP) is essential for continuous professional development in family medicine. A CoP in medical education comprises a group of individuals who share common interests and learn and share knowledge, skills, and attitudes. The generation gap between teachers and learners can influence their effective collaboration in family medicine education. To address this issue, shared reading, which involves group discussions on medical texts, effectively promotes intergenerational learning within a CoP. Shared reading is particularly beneficial in rural contexts, where medical resources are scarce, and the generation gap between teachers and learners is wide. This study examines how shared reading facilitates learning and practice among family medicine trainees in rural areas. Methods This qualitative study utilized a grounded-theory approach. It involved the participation of eight family medicine residents, five junior residents, and seven medical students from Unnan City Hospital, Japan, in shared reading sessions between August 2022 and March 2023. Semi-structured interviews were conducted with all participants after the sessions. Results Data analysis using the grounded-theory approach yielded three themes: facilitating learning and motivation, respectful collaboration with teachers, and developing a relationship that fosters psychological safety. First, through shared reading, the participants had opportunities to learn about medical issues and engage in continual dialogues with colleagues and teachers. Second, the participants felt motivated to apply their newly acquired knowledge at work and collaborate with teachers. Third, they acquired self-regulated learning skills by adapting their motivations to their interests and experiences. Increased interaction between participants and teachers during the sessions helped mitigate the generation gap and enhanced psychological safety. Conclusions Shared reading effectively promotes continuous learning and motivates medical learners to apply their knowledge and collaborate with teachers. It facilitates the development of self-regulated learning skills, helps mitigate the generation gap, and enhances psychological safety among educators and learners in rural medical education.

Retracted: Named Entity Recognition of Medical Text Based on the Deep Neural Network.

[This retracts the article DOI: 10.1155/2022/3990563.].

Spezifika der Übersetzung von medizinischen Texten

Summary The study deals with the issue of translation in the field of medicine. The first part contains a brief theoretical introduction into medical translation in general. It is followed by a practical analysis of problems in the translation from the source to the target language with a focus on abbreviations and defects in source texts. It describes specific problem areas and examples which occur very often in medical texts and can lead to errors in target texts. Decoding medical abbreviations and defects in source texts in the German – Slovak language pair is a very demanding job, because the use of Latin, Greek or English words and their abbreviations in medical texts is a common occurrence for doctors, but can be a source of problems and confusion for translators.

MITER: Medical Image–TExt joint adaptive pretRaining with multi-level contrastive learning

Recently multimodal medical pretraining models play a significant role in automatic medical image and text analysis that has wide social and economical impact in healthcare. Despite being able to be quickly transferred to downstream tasks, the models are greatly limited due to the fact that these models can only be pretrained with professional medical image–text datasets, which usually contain a very small number of samples. In this work we propose MITER (Medical Image–Text Joint adaptive Pretraining), a joint adaptive pretraining framework via multi-level contrastive learning to overcome this limitation by pretraining image and text models for medical domain and utilizing existing models pretrained on generic data, which contain enormous number of samples. MITER features two types of objectives to solve the problem. The first type is uni-modal objectives that pretrain the models with medical images and text separately on uni-modal tasks. The other type is a cross-modal objective that pretrains jointly, allowing the models to influence each other on cross-modal tasks. We also introduce a strategy to dynamically select hard negative samples during the training process for better performance. Experimental results over four medical tasks, image-report retrieval, multi-label image classification, visual question answering, and report generation, show that our MITER framework solves the limitation problem by greatly outperforming existing benchmark models on all the tasks. The source code of our framework is available online.22https://github.com/ZhuYi98/MITER.

Effects of Lifestyle Interventions on Cardiovascular Disease Risk and Risk Factors Among Individuals at High Risk for Type 2 Diabetes: Protocol for a Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Individuals at high risk for type 2 diabetes are also at an increased risk for developing cardiovascular disease (CVD). Although there are separate trials examining the effects of lifestyle interventions on absolute CVD risk among people at high risk for type 2 diabetes, a comprehensive evidence synthesis of these trials is lacking. We will systematically synthesize the evidence on the effects of lifestyle interventions in reducing absolute CVD risk and CVD risk factors among people at high risk for type 2 diabetes. We adhered to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) statement in reporting the details of this protocol. Randomized controlled trials of diabetes prevention that examined the effects of lifestyle interventions for at least 6 months on absolute CVD risk and CVD risk factors among individuals at high risk for type 2 diabetes will be eligible. We will systematically search the MEDLINE, Embase, PsycINFO, CENTRAL, and Scopus databases and ClinicalTrials.gov using a mix of Medical Subject Headings and text words. Two authors will independently screen the abstract and title of the articles retrieved from the search, followed by full-text reviews using the inclusion and exclusion criteria and data extraction from the eligible studies. Article screening and data extraction will be performed in the Covidence software. The primary outcome will be the changes in absolute 10-year CVD risk, as estimated by risk prediction models. The secondary outcomes are the changes in CVD risk factors, including behavioral, clinical, biochemical, and psychosocial risk factors, and incidence of type 2 diabetes. An initial database search was conducted in July 2023. After screening 1935 articles identified through the database search, 42 articles were considered eligible for inclusion. It is anticipated that the study findings will be submitted for publication in a peer-reviewed journal by the end of 2024. This study will provide up-to-date, systematically synthesized evidence on the effects of lifestyle interventions on absolute CVD risk and CVD risk factors among individuals at high risk for type 2 diabetes. PROSPERO CRD42023429869; https://tinyurl.com/59ajy7rw. DERR1-10.2196/53517.

THE EFFECTS OF TRANSLATION IN TEACHING ESP MEDICAL READING FOR IN-SERVICE STUDENTS AT CAN THO UNIVERSITY OF MEDICINE AND PHARMACY, VIETNAM

English for special purposes (ESP) medical English has been offered as a course at Can Tho University of Medicine and Pharmacy (CTUMP), Can Tho City, for several years. Until now, to most of the in-service students, reading in medical English poses a significant challenge due to their proficiency level in English and limited knowledge of vocabulary. They struggle to absorb the medical texts, especially medical terms properly, discouraging them from engaging with medical English documents. There has been a renewed interest in using translation (English-Vietnamese) in medical English classes to support learners. Therefore, the objectives of this paper are to examine the difficulties faced by weaker students in learning medical English and to explore their perceptions of teachers using translation in teaching ESP medical reading. The study also investigates the importance of translation as a pedagogical tool in motivating weak in-service students to learn reading and comprehend terminologies and texts. The research results were computed by employing the Statistical Package for the Social Sciences (SPSS) software. The findings revealed weak in-service students’ significant problems, their positive attitudes towards translation as well as their pedagogical role in ESP medical English classes.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0056/a.php" alt="Hit counter" /></p>

Diagnostic Performance Evaluation of Deep Learning-Based Medical Text Modelling to Predict Pulmonary Diseases from Unstructured Radiology Free-Text Reports

Diagnostic Performance Evaluation of Deep Learning-Based Medical Text Modelling to Predict Pulmonary Diseases from Unstructured Radiology Free-Text Reports

Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data

The task of accurately identifying medical entities and extracting entity relationships from large-scale medical text data has become a hot topic in recent years, aiming to mine potential rules and knowledge. How to conduct in-depth context analysis from biomedical texts, such as medical procedures, diseases, therapeutic drugs, and disease characteristics, and identify valuable knowledge in the medical field is our main research content. Through the process of knowledge mining, a deeper understanding of the complex relationships between various factors in diseases can be gained, which holds significant guiding implications for clinical research. An approach based on context semantic analysis is proposed to realize medical entity recognition and entity relationship extraction. In addition, we build a medical knowledge base related to coronary heart disease and combine the NCBI disease dataset and the medical lexicon dataset extracted from the text as the test data of the experiment. Experimental results show that this model can effectively identify entities in medical text data; the WBC model achieved an F1 score of 89.2% in the experiment, while the CSR model achieved an F1 score of 83.4%, and the result is better than other methods.

Popularization of Medical Knowledge in German Journal Discourse in Perspective of Language Code

A study was conducted on the language code used in medical popular science texts in German journal discourse. Interviews, brief news texts, and articles on medical topics are used as empirical material. It is shown that the arsenal of modern German language resources is aimed at implementing communicative-pragmatic functions of popular science discourse texts as a whole. It is noted that the leading functions among them are informing, influencing, and persuading the target audience. It is established that verbal means of different linguistic levels, as well as a number of stylistic devices (inversion, parceling, allusion, metaphor, metonymy, personification), not only convey medical knowledge to wide segments of the population but also transmit information about the national-cultural specifics of Germany and its achievements in medicine, taking into account the age characteristics of the addressees, also performing an educational function and seeking to achieve emotional involvement of the reader in complex content. The relevance of the study is due to the growing interest of non-professional participants in media-medical communication in the processes of self-diagnosis and self-treatment, which require mastering various medical knowledge. The novelty of the research lies in determining the pragmatic potential of structural-compositional features, including clickable headlines, and linguistic means, thanks to which a special media-medical picture of the world is formed.

ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports

ObjectivesTo assess the quality of simplified radiology reports generated with the large language model (LLM) ChatGPT and to discuss challenges and chances of ChatGPT-like LLMs for medical text simplification.MethodsIn this exploratory case study, a radiologist created three fictitious radiology reports which we simplified by prompting ChatGPT with “Explain this medical report to a child using simple language.” In a questionnaire, we tasked 15 radiologists to rate the quality of the simplified radiology reports with respect to their factual correctness, completeness, and potential harm for patients. We used Likert scale analysis and inductive free-text categorization to assess the quality of the simplified reports.ResultsMost radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed relevant medical information, and potentially harmful passages were reported.ConclusionWhile we see a need for further adaption to the medical field, the initial insights of this study indicate a tremendous potential in using LLMs like ChatGPT to improve patient-centered care in radiology and other medical domains.Clinical relevance statementPatients have started to use ChatGPT to simplify and explain their medical reports, which is expected to affect patient-doctor interaction. This phenomenon raises several opportunities and challenges for clinical routine.Key Points• Patients have started to use ChatGPT to simplify their medical reports, but their quality was unknown.• In a questionnaire, most participating radiologists overall asserted good quality to radiology reports simplified with ChatGPT. However, they also highlighted a notable presence of errors, potentially leading patients to draw harmful conclusions.• Large language models such as ChatGPT have vast potential to enhance patient-centered care in radiology and other medical domains. To realize this potential while minimizing harm, they need supervision by medical experts and adaption to the medical field.Graphical

A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification.

Given the threat posed by cancer to human health, there is a rapid growth in the volume of data in the cancer field and interdisciplinary and collaborative research is becoming increasingly important for fine-grained classification. The low-resolution classifier of reported studies at the journal level fails to satisfy advanced searching demands, and a single label does not adequately characterize the literature originated from interdisciplinary research results. There is thus a need to establish a multilabel classifier with higher resolution to support literature retrieval for cancer research and reduce the burden of screening papers for clinical relevance. The primary objective of this research was to address the low-resolution issue of cancer literature classification due to the ambiguity of the existing journal-level classifier in order to support gaining high-relevance evidence for clinical consideration and all-sided results for literature retrieval. We trained a multilabel classifier with scalability for classifying the literature on cancer research directly at the publication level to assign proper content-derived labels based on the "Bidirectional Encoder Representation from Transformers (BERT) + X" model and obtain the best option for X. First, a corpus of 70,599 cancer publications retrieved from the Dimensions database was divided into a training and a testing set in a ratio of 7:3. Second, using the classification terminology of International Cancer Research Partnership cancer types, we compared the performance of classifiers developed using BERT and 5 classical deep learning models, such as the text recurrent neural network (TextRNN) and FastText, followed by metrics analysis. After comparing various combined deep learning models, we obtained a classifier based on the optimal combination "BERT + TextRNN," with a precision of 93.09%, a recall of 87.75%, and an F1-score of 90.34%. Moreover, we quantified the distinctive characteristics in the text structure and multilabel distribution in order to generalize the model to other fields with similar characteristics. The "BERT + TextRNN" model was trained for high-resolution classification of cancer literature at the publication level to support accurate retrieval and academic statistics. The model automatically assigns 1 or more labels to each cancer paper, as required. Quantitative comparison verified that the "BERT + TextRNN" model is the best fit for multilabel classification of cancer literature compared to other models. More data from diverse fields will be collected to testify the scalability and extensibility of the proposed model in the future.

CLART: A cascaded lattice-and-radical transformer network for Chinese medical named entity recognition

Chinese medical named entity recognition (NER) is a fundamental task in Chinese medical natural language processing, aiming to recognize Chinese medical entities within unstructured medical texts. However, it poses significant challenges mainly due to the extensive usage of medical terms in Chinese medical texts. Although previous studies have made attempts to incorporate lexical or radical knowledge in order to improve the comprehension of medical texts, these studies either focus solely on one of these aspects or utilize a basic concatenation operation to combine these features, which fails to fully utilize the potential of lexical and radical knowledge. In this paper, we propose a novel Cascaded LAttice-and-Radical Transformer (CLART) network to exploit both lexical and radical information for Chinese medical NER. Specifically, given a sentence, a medical lexicon, and a radical dictionary, we first construct a flat lattice (i.e., character-word sequence) for the sentence and radical components of each Chinese character through word matching and radical parsing, respectively. We then employ a lattice Transformer module to capture the dense interactions between characters and matched words, facilitating the enhanced utilization of lexical knowledge. Subsequently, we design a radical Transformer module to model the dense interactions between the lattice and radical features, facilitating better fusion of the lexical and radical knowledge. Finally, we feed the updated lattice-and-radical-aware character representations into a Conditional Random Fields (CRF) decoder to obtain the predicted labels. Experimental results conducted on two publicly available Chinese medical NER datasets show the effectiveness of the proposed method.

Leveraging Summary Guidance on Medical Report Summarization.

This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50 K, 16 K and 378 K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the created datasets with pre-trained encoder-decoder language models, including BERT2BERT, BERTShare, RoBERTaShare, Pegasus, ProphetNet, T5-large, BART and GSUM. Further, based on the BART model, we leverage the sampled summaries from the training set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method.

FEATURES OF TEACHING RUSSIAN AS A FOREIGN LANGUAGE: SCHEME OF STYLISTIC ANALYSIS OF SCIENTIFIC TEXT

Features of teaching Russian as a foreign language to students from the former republics of the Soviet Union and other countries, difficulties of learning Russian as a foreign language and some ways of overcoming them are considered in the article. One of the priority areas of work with students from other countries when considering the scientific style of the modern Russian language at the university is the stylistic analysis of the scientific text of the specialty. The article provides an approximate scheme of stylistic analysis of a scientific text (on the example of a medical text of a scientific and educational substyle) for use in classes in Russian as a non-native, Russian as a foreign language, which includes the lexical, morphological and syntactic levels of the Russian language and also the stylistic features of a scientific text.

Acceptance and rejection of medical knowledge : Latin translation of Arabic medical texts during the 11th~14th centuries

Acceptance and rejection of medical knowledge : Latin translation of Arabic medical texts during the 11th~14th centuries

Medical Terminology and the Reading of Medical English Texts

The study aims to explicit the importance of medical terminology in reading medical English texts and learn to understand a medical term by word-formation pattern and apply this into reading. By referring to various works and papers about medical terminology and reading medical English texts, their relationship was found. Upon this, word-formation pattern was used to decompound the word parts of medical terms, and the study was concluded with the application of this pattern in reading medical English texts. The study shows that characteristics of medical terminology can help a lot in understanding the complexity of medical terminology. Word-formation pattern enables readers to recognize prefixes, roots, and affixes of medical terminology and decomposed long and discouraging words. Word-formation pattern was useful in the understanding of both medical terminology and medical English texts. It could highly improve learners’ efficiency in remembering medical terms and in reading medical English texts.