Types Of Questions Research Articles

Comparative Analysis of Performance of Large Language Models in Urogynecology.

Despite growing popularity in medicine, data on large language models in urogynecology are lacking. The aim of this study was to compare the performance of ChatGPT-3.5, GPT-4, and Bard on the American Urogynecologic Society self-assessment examination. The examination features 185 questions with a passing score of 80. We tested 3 models-ChatGPT-3.5, GPT-4, and Bard on every question. Dedicated accounts enabled controlled comparisons. Questions with prompts were inputted into each model's interface, and responses were evaluated for correctness, logical reasoning behind answer choice, and sourcing. Data on subcategory, question type, correctness rate, question difficulty, and reference quality were noted. The Fisher exact or χ2 test was used for statistical analysis. Out of 185 questions, GPT-4 answered 61.6% questions correctly compared with 54.6% for GPT-3.5 and 42.7% for Bard. GPT-4 answered all questions, whereas GPT-3.5 and Bard declined to answer 4 and 25 questions, respectively. All models demonstrated logical reasoning in their correct responses. Performance of all large language models was inversely proportional to the difficulty level of the questions. Bard referenced sources 97.5% of the time, more often than GPT-4 (83.3%) and GPT-3.5 (39%). GPT-3.5 cited books and websites, whereas GPT-4 and Bard additionally cited journal articles and society guidelines. Median journal impact factor and number of citations were 3.6 with 20 citations for GPT-4 and 2.6 with 25 citations for Bard. Although GPT-4 outperformed GPT-3.5 and Bard, none of the models achieved a passing score. Clinicians should use language models cautiously in patient care scenarios until more evidence emerges.

Assessing Generative Pretrained Transformers (GPT) in Clinical Decision-Making: Comparative Analysis of GPT-3.5 and GPT-4.

Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors' and residents' ratings, and specific question types. A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications. Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5's accuracy, beneficial, and completeness dimensions. ChatGPT's potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.

Multimodal ChatGPT-4V for Electrocardiogram Interpretation: Promise and Limitations.

This study evaluated the capabilities of the newly released ChatGPT-4V, a large language model with visual recognition abilities, in interpreting electrocardiogram waveforms and answering related multiple-choice questions for assisting with cardiovascular care.

Разработка онлайн-сервиса для обработки информации о проверке знаний сотрудников компании

In today's environment, companies have to face great competition in rapidly growing industry segments or market areas. This is why managing employee knowledge is key task in a business environment. Organizations devote time not only to training personnel, but also to working out theoretical knowledge in practice, analyzing errors that arise during communication with the client. Companies need to look for effective methods to assess their employees' knowledge in order to increase competitiveness, but existing platforms do not provide all the necessary opportunities. Therefore, there is a need to create highly targeted software that would make it possible to form tests for the knowledge assessment of organization employees and analyze the results obtained. This article presents the main stages of an online service creating for the information processing about the testing results of the company employees knowledge. Each employee has access to his personal account, in which he can perform tests, track results and rating among other employees by average score, and receive feedback from the manager. The head of the company has the ability to track information for each employee, assign tests, comment on answers, and delete or add new employees. When creating tests, it is possible to select the question type, including downloading audio files and images. The convenient menu bar has been created, which contains various tabs corresponding to the user role. The database has been developed and the relationships types between tables have been defined. User accounts have been created to assign appropriate access levels.

A Review of Research on Error Correction in English in College Entrance Examination in China from 2013-2022

Error correction in English is the constructional question type of the compulsory English test in the college entrance examination, including modification, addition and deletion, and the content involves morphology, grammar and syntax. In this paper, from two aspects of research methods and research content, quantitative and qualitative analysis methods are used to review the research papers on essay error correction in CNKI academic journals. The results show that the research method is single and the research tool is simple. In terms of research content, there are some defects in the depth and breadth of domestic essay error correction research, the proportion of introduction and problem-solving skills is too large, and the proportion of empirical research is insufficient, which needs to be expanded in depth. Finally, combined with the latest development of foreign error correction research, the development direction of the research is prospected from the aspects of research content, level and research methods, and some suggestions and references are provided for future related research.

Perceived Challenges and Ways to Improve Reading Proficiency for EFL Teachers: Narratives from EFL Teachers

The need of using English in global context has led schools in Indonesia to prepare and make their students proficient in English. However, the majority of English teachers may not have attained a level of proficiency deemed necessary for effective teaching. This study aimed to reveal the level of teachers’ reading proficiency and to figure out how they perceived challenges and ways to improve their proficiency. Qualitative data was used as a method of inquiry. The study employed a narrative inquiry to dig deeper into teachers’ perspective. A proficiency test, self-reflection (logbook), and interview were used as the instrument to measure 15 EFL teachers’ proficiency and figure out their challenges and ways to improve their proficiency. The data was analyzed using inductive reflective thematic analysis and the findings showed that teachers proficiency varied, but most of them have a good reading proficiency equal to B2-CEFR level; It is referred to as upper intermediate and independent user by CEFR. In addition, five themes expressing the teachers’ perceived challenges were revealed: 1) They experienced self-errors; 2) The answer is implied; 3) The text is difficult; 4) The question type is unfamiliar; 5) They encountered unfamiliar vocabulary. Moreover, the ways the teachers used to improve their proficiency were: 1) understand the question and passage carefully; 2) find main idea and supporting details; 3) familiarize with the topic; 4) Enrich vocabulary; 5) scan and take notes; 6) predict the question and keep focus. In conclusion, the findings provide the important insights into EFL teachers. Their collective experiences can be used to overcome the challenges faced by EFL teachers.

ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology-Head and Neck Surgery.

The recent surge in popularity of large language models (LLMs), such as ChatGPT, has showcased their proficiency in medical examinations and potential applications in health care. However, LLMs possess inherent limitations, including inconsistent accuracy, specific prompting requirements, and the risk of generating harmful hallucinations. A domain-specific model might address these limitations effectively. Developmental design. Virtual. Otolaryngology-head and neck surgery (OHNS) relevant data were systematically gathered from open-access Internet sources and indexed into a knowledge database. We leveraged Retrieval-Augmented Language Modeling to recall this information and utilized it for pretraining, which was then integrated into ChatGPT4.0, creating an OHNS-specific knowledge question & answer platform known as ChatENT. The model is further tested on different types of questions. ChatENT showed enhanced performance in the analysis and interpretation of OHNS information, outperforming ChatGPT4.0 in both the Canadian Royal College OHNS sample examination questions challenge and the US board practice questions challenge, with a 58.4% and 26.0% error reduction, respectively. ChatENT generated fewer hallucinations and demonstrated greater consistency. To the best of our knowledge, ChatENT is the first specialty-specific knowledge retrieval artificial intelligence inthe medical field that utilizes the latest LLM. It appears to have considerable promise in areas such as medical education, patient education, and clinical decision support. The model has demonstrated the capacity to overcome the limitations of existing LLMs, thereby signaling a future of more precise, safe, and user-friendly applications in the realm of OHNS and other medical fields.

Deep Cell-Type Deconvolution from Bulk Gene Expression Data Using DECODE

It is becoming clear that bulk gene expression measurements represent an average over very different cells. Elucidating the expression and abundance of each of the encompassed cells is key to disease understanding and precision medicine approaches. A first step in any such deconvolution is the inference of cell type abundances in the given mixture. Numerous approaches to cell-type deconvolution have been proposed, yet very few take advantage of the emerging discipline of deep learning and most approaches are limited to input data regarding the expression profiles of the cell types in question. Here we present DECODE, a deep learning method for the task that is data-driven and does not depend on input expression profiles. DECODE builds on a deep unfolded non-negative matrix factorization technique. It is shown to outperform previous approaches on a range of synthetic and real data sets, producing abundance estimates that are closer to and better correlated with the real values.

Using an AI-based avatar for interviewer training at Children's Advocacy Centers: Proof of Concept.

This proof-of- concept study focused on interviewers' behaviors and perceptions when interacting with a dynamic AI child avatar alleging abuse. Professionals (N = 68) took part in a virtual reality (VR) study in which they questioned an avatar presented as a child victim of sexual or physical abuse. Of interest was how interviewers questioned the avatar, how productive the child avatar was in response, and how interviewers perceived the VR interaction. Findings suggested alignment between interviewers' virtual questioning approaches and interviewers' typical questioning behavior in real-world investigative interviews, with a diverse range of questions used to elicit disclosures from the child avatar. The avatar responded to most question types as children typically do, though more nuanced programming of the avatar's productivity in response to complex question types is needed. Participants rated the avatar positively and felt comfortable with the VR experience. Results underscored the potential of AI-based interview training as a scalable, standardized alternative to traditional methods.

Health information-seeking on Reddit, by people who use opioids

Introduction. In the most recent year that data is available, 101,035 people died from drug overdoses in the United States. For people who use drugs, obtaining treatment remains a significant barrier due to issues of fear, stigmatisation, and illegality. Some people turn to the anonymous social media site, Reddit, for obtaining health and harm reduction information. Method. In this study we analysed 2,748 health-related posts on the subreddit r/opiates over a three-month period. Analysis. Using a thematic analysis grounded in social constructivism, we categorised the data to determine the most frequent types of health questions asked by people who use opioids. We then used the text analysis software Linguistic Inquiry and Word Count to determine the emotional content of the posts. Results. Most of the posts were asking about harm reduction strategies and methods to reduce or quit opioid use. Conclusion. The text analysis demonstrated evidence of Foucault’s discourse theory. People who use opioids need more information on harm reduction strategies, and especially safely administering and withdrawing from them.

Food waste prevention and reduction: Practices, cultural and personal determinants

Food waste is a pressing global issue with profound social, environmental, and economic implications, prompting an urgent need for a comprehensive understanding of its sources. In the current study, we explored the role of cultural and personal determinants in food management practices. We operationalized two food practices – prevention of food waste by inventory and meal management and reduction of food waste by leftovers management, and explored how they are shaped by personal and cultural determinants. We asked 1200 respondents, three types of questions about their (1) cultural and personal attributes, such as their ethical and religious values; (2) lifestyle and dietary habits, such as their frequency of eating out, and (3) sociodemographic characteristics. We modeled the association between these three types of questions and the two food management practices utilizing a hierarchical multiple regression model. The results indicate that ascribing significant importance to hospitality is associated with less proficiency in inventory and meal management. Those who place a high priority on hospitality preparedness are proficient in managing leftovers but less proficient in inventory and meal management. In addition, individuals with strong ethical and religious views against food wastage tend to manage their inventory and meals poorly but excel at handling surplus food. Finally, secular Ashkenazi Israelis tend to engage in better practices to prevent and reduce food waste. The results also stress the differences between practices to prevent food waste and those to reduce it, opening a promising avenue for future research.

The Art of Creative Inquiry—From Question Asking to Prompt Engineering

ABSTRACTAs artificial intelligence and natural language processing methods rapidly develop, communication plays a pivotal role in every‐day interactions. In this theoretical paper, we explore the overlap and commonalities between question‐asking and prompt engineering. While seemingly distinct, these processes share a common foundation in essential skills like creativity, critical thinking, and cognitive flexibility. We contend that prompt engineering, the art of crafting cues for language models, and question‐asking, the skill of formulating inquiries, form a symbiotic relationship. Delving into question complexity through Bloom's taxonomy and diverse types of questions, we propose strategies for not only efficient but also engaging prompt design. Our theoretical contribution emphasizes the dynamic role of creativity in both processes, offering intriguing perspectives on human–machine interactions and advancing our understanding of language models and communication skills.

Response Rates by Condom Use Measurement Type Among Sexually Active Young Black Men Who Have Sex with Women.

Measuring condom use accurately is important to understanding sexual behaviors; however, there is still no consensus on the gold standard of condom use measurement. In this study, authors aimed to investigate if non-response rates were higher when absolute or relative frequency condom use questions were utilized. This study used Check It study data of Black men who have sex with women, ages 15-26 in New Orleans, LA. All men underwent a survey of sexual behavior, which included questions on condom use. From January 2017 to August 2019, participants were asked for the number of sex acts during which they had used a condom (absolute frequency question), and after August 2019 until March 2020, this was changed to response options of "Never", "Sometimes" or "Always" (relative frequency question). Participants could refuse to answer the question; the rate of refusal was tabulated by question type to determine if non-response rates differed. There were 1907 participants included in the study, with data on 2957 partnerships. Of those asked the relative frequency question, 92.7% responded compared to 86.1% who were asked the absolute frequency. In GEE modeling, those asked the relative frequency question had 2.42 (95% C.I. 1.42, 4.13, p < 0.001) greater odds of response than those asked the absolute frequency question. Relative frequency questions may be more likely to elicit a response, in research among this population.

Communication and collaboration skills training in radiation oncology: A quantitative validation survey.

Communication and collaboration are integral in radiation oncology practice. A recently published qualitative study identified several deficiencies in skills development for Australian/New Zealand trainees. We aim to validate these findings to guide curriculum development. A quantitative survey was developed through an iterative process, using themes identified in the previous qualitative investigation. This survey was distributed to radiation oncologists and trainees across Australia and New Zealand via email. Data collection and management utilised the REDCap system. Question types varied to maximise richness of data, including ranking, likert-scales and free-text questions. Results are primarily reported descriptively. Totally 35 participants submitted completed survey responses with broad representation across geography, gender and clinician seniority. To learn communication, participants reported strong preferences towards informal observation (60% agreement) and self-reflection (49% agreement), and against online learning (77% disagreement) methodologies. Nearly 35% acknowledge poor communication at least weekly, with time pressure being a major barrier (63% agreement). Clinical uncertainty and existing patient/family assumptions (both 74% agreement) contribute to difficulties in breaking bad news, with online learning being the only negatively perceived training modality (23% agreement). No participants reported any formal training/mentoring in multi-disciplinary team (MDT) engagement. Conflict was commonly witnessed/experienced (97%) and 26% of participants avoid MDTs due to difficulties experienced. This study validates the themes previously identified. We identified a strong preference for informal learning methodologies and against online modules, discordant to published literature. Effective collaboration within MDTs is identified as a particular area of need. We recommend future curriculum modification considers these results to maximise efficacy.

PERCEPTIONS, PRACTICES, AND ASSESSMENT LITERACY NEEDS OF EFL TEACHERS: ENHANCING READING LITERACY INSTRUCTION UNDER AKM POLICY

Since the discontinuation of national examinations (UN) in 2020, the Indonesian Government introduced the Minimum Competency Assessment (Asesmen Kompetensi Minimum-AKM) to assess students' fundamental abilities. Consequently, teachers are pivotal in integrating AKM-based reading literacy questions to enhance students' reading skills. This study aims to explore teachers' perceptions, practices, and assessment literacy needs concerning teaching and assessing students' reading literacy based on AKM. Ten EFL junior high school teachers participated in interviews. Findings revealed varying perceptions among teachers regarding AKM. While some supported the policy for its positive impact on students and learning, others doubted its effectiveness for certain reasons. In terms of teaching and assessment practices, most teachers partially integrate the three components of AKM reading literacy into their classrooms. They also utilize various stimuli and question types similar to those in AKM, yet complex multiple-choice assessments are underutilized. Lastly, teachers expressed diverse literacy assessment needs, including strategies for teaching reading, designing critical reading questions, creating stimulus materials, gaining AKM knowledge, enhancing student interest in reading, and identifying intended learning outcomes to improve their teaching and evaluation of students' literacy.

Content Analysis : Evaluation of English Workbook Based on Curriculum 2013

Purpose of the study: The aim of this research is to find out whether the quality of the English Workbook published by PT. Swadaya Murni used by MTs 13 Jakarta meets the 2013 curriculum criteria Methodology: The researcher used a qualitative method with a content analysis design as a way for researchers to disseminate the quality of English Workbook. The instrument of this research is the BSNP (National Education Standards Board) assessment rubric and to make this research more valid, the researcher conducted several interviews with teachers and several students at MTs 13 Jakarta who used the Workbook. PT. Main Findings: The results of this research only reached 31.25%, which means that the Workbook did not meet the BSNP standard criteria. The results of the interviews also showed that the explanation of the Workbook did not satisfy the teachers and students. However, these worksheets meet students' English language needs because they use language that is appropriate to the context being studied and appropriate to the students' level of knowledge. Novelty/Originality of this study: The novelty of this research reveals that the evaluation of English practice books based on the 2013 Curriculum does not only pay attention to the suitability of the material to the curriculum, but also considers aspects such as depth of understanding of concepts, diversity of question types, creativity in presenting material, and relevance to students' needs in the digital era. At the moment.

How well do large language model-based chatbots perform in oral and maxillofacial radiology?

This study evaluated the performance of four large language model (LLM)-based chatbots by comparing their test results with those of dental students on an oral and maxillofacial radiology examination. ChatGPT, ChatGPT Plus, Bard, and Bing Chat were tested on 52 questions from regular dental college examinations. These questions were categorized into three educational content areas: basic knowledge, imaging and equipment, and image interpretation. They were also classified as multiple-choice questions (MCQs) and short-answer questions (SAQs). The accuracy rates of the chatbots were compared with the performance of students, and further analysis was conducted based on the educational content and question type. The students' overall accuracy rate was 81.2%, while that of the chatbots varied: 50.0% for ChatGPT, 65.4% for ChatGPT Plus, 50.0% for Bard, and 63.5% for Bing Chat. ChatGPT Plus achieved a higher accuracy rate for basic knowledge than the students (93.8% vs. 78.7%). However, all chatbots performed poorly in image interpretation, with accuracy rates below 35.0%. All chatbots scored less than 60.0% on MCQs, but performed better on SAQs. The performance of chatbots in oral and maxillofacial radiology was unsatisfactory. Further training using specific, relevant data derived solely from reliable sources is required. Additionally, the validity of these chatbots' responses must be meticulously verified.

EDUCATIONAL DATA MINING AND LEARNING ANALYTICS: TEXT GENERATORS USAGE EFFECT ON STUDENTS’ GRADES

Today, various types of data are constantly growing, so they can be used for different purposes. In this investigation, educational data has been analyzed to determine the influence of assessment on student knowledge. The newly collected dataset has been prepared and statistically analyzed. The dataset consists of open-question answers collected on one study subject during the midterm exam at Vilnius Gediminas Technical University. The results of the statistical analysis have shown that by using the text generators, students obtained higher grades by paraphrasing the answers to the questions in good quality. Furthermore, research has shown which types of questions are more difficult for students to answer without additional material and using text generation tools. It can be useful for lecturers planning course assessment tasks.

A Comparative Analysis of ChatGPT, ChatGPT-4, and Google Bard Performances at the Advanced Burn Life Support Exam.

Artificial intelligence and large language models (LLMs) have recently gained attention as promising tools in various healthcare domains, offering potential benefits in clinical decision-making, medical education, and research. The Advanced Burn Life Support (ABLS) program is a didactic initiative endorsed by the American Burn Association, aiming to provide knowledge on the immediate care of severely burn patients. The aim of the study was to compare the performance of three LLMs (ChatGPT-3.5, ChatGPT-4, and Google Bard) on the ABLS exam. The ABLS exam consists of 50 questions with 5 multiple-choice answers. The passing threshold is 80% of correct answers. The 3 LLMs were queried with the 50 questions included in the latest version of the ABLS exam, on July 18th, 2023. ChatGPT-3.5 scored 86% (43 out of 50), ChatGPT-4 scored 90% (45 out of 50), and Bard scored 70% (35 out of 50). No difference was measured between ChatGPT-3.5 and ChatGPT-4 (P = .538) and between ChatGPT-3.5 and Bard (P = .054), despite the borderline P-value. ChatGPT-4 performed significantly better than Bard (P = .012). Out of the 50 questions, 78% (n = 39) were direct questions, while 12% (n = 11) were presented as clinical scenarios. No difference in the rate of wrong answers was found based on the type of question for the 3 LLMs. ChatGPT-3.5 and ChatGPT-4 demonstrated high accuracy at the ABLS exam and outperformed Google Bard. However, the potential multiple applications of LLMs in emergency burn and trauma care necessitate appropriate surveillance and most likely should represent a tool to complement human cognition.

Verses of Impossibility in the Holy Quran: A Grammatical and Rhetorical Analysis

Verses of impossibility are those that refer to events that can never or rarely happen, tasks that are difficult or impossible to perform, people or things that are of no use, things that are impossible to find or futile efforts. Impossibilities can be classified into logical, physical, causal, nomological, temporary, permanent, partial, total, objective or subjective impossibility. This study sought to analyze verses of impossibility in the Holy Quran, their grammatical structure, rhetorical features, types of questions and conditional, emphatic, and/or negative particles used. Results of the analysis showed two kinds of impossibility (i) those in which Allah challenges the disbelievers and those in which the disbelievers ask the Prophets for what they consider impossible demands. In addition, verses of impossibility in the current study use a combination of grammatical structures and rhetorical devices that mainly contain conditional sentences beginning with conditional particles أن /?an/ (if), لو ,حتى /Hatta:/ (until), لولا /laula:/ (if), negative, imperative, interrogative, and emphatic structures, few modals and rhetorical questions, some metaphors, hyperbole, antitheses and counterfactuals to describe challenges, futile efforts, stubbornness of the disbelievers and their incongruous behavior. They also focus on logical, temporary and permanent impossibilities. Other rhetorical features of the Holy Quran, such as antiphrasis, asyndeton, cadence, chiasmus, epizeuxis, equivoque, isocolon, palindrome, polyptoton, and synecdoche are still open for further investigation by future researchers.