Readability Assessment Research Articles

Exploring the Effectiveness of Shallow and L2 Learner-Suitable Textual Features for Supervised and Unsupervised Sentence-Based Readability Assessment

Simplicity in information found online is in demand from diverse user groups seeking better text comprehension and consumption of information in an easy and timely manner. Readability assessment, particularly at the sentence level, plays a vital role in aiding specific demographics, such as language learners. In this paper, we research model evaluation metrics, strategies for model creation, and the predictive capacity of features and feature sets in assessing readability based on sentence complexity. Our primary objective is to classify sentences as either simple or complex, shifting the focus from entire paragraphs or texts to individual sentences. We approach this challenge as both a classification and clustering task. Additionally, we emphasize our tests on shallow features that, despite their simplistic nature and ease of use, seem to yield decent results. Leveraging the TextStat Python library and the WEKA toolkit, we employ a wide variety of shallow features and classifiers. By comparing the outcomes across different models, algorithms, and feature sets, we aim to offer valuable insights into optimizing the setup. We draw our data from sentences sourced from Wikipedia’s corpus, a widely accessed online encyclopedia catering to a broad audience. We strive to take a deeper look at what leads to greater readability classification in datasets that appeal to audiences such as Wikipedia’s, assisting in the development of improved models and new features for future applications with low feature extraction/processing times.

Assessment of the Quality and Readability of Web-Based Arabic Health Information on Halitosis: Infodemiological Study.

Halitosis, characterized by an undesirable mouth odor, represents a common concern. This study aims to assess the quality and readability of web-based Arabic health information on halitosis as the internet is becoming a prominent global source of medical information. A total of 300 Arabic websites were retrieved from Google using 3 commonly used phrases for halitosis in Arabic. The quality of the websites was assessed using benchmark criteria established by the Journal of the American Medical Association, the DISCERN tool, and the presence of the Health on the Net Foundation Code of Conduct (HONcode). The assessment of readability (Flesch Reading Ease [FRE], Simple Measure of Gobbledygook, and Flesch-Kincaid Grade Level [FKGL]) was conducted using web-based readability indexes. A total of 127 websites were examined. Regarding quality assessment, 87.4% (n=111) of websites failed to fulfill any Journal of the American Medical Association requirements, highlighting a lack of authorship (authors' contributions), attribution (references), disclosure (sponsorship), and currency (publication date). The DISCERN tool had a mean score of 34.55 (SD 7.46), with the majority (n=72, 56.6%) rated as moderate quality, 43.3% (n=55) as having a low score, and none receiving a high DISCERN score, indicating a general inadequacy in providing quality health information to make decisions and treatment choices. No website had HONcode certification, emphasizing the concern over the credibility and trustworthiness of these resources. Regarding readability assessment, Arabic halitosis websites had high readability scores, with 90.5% (n=115) receiving an FRE score ≥80, 98.4% (n=125) receiving a Simple Measure of Gobbledygook score <7, and 67.7% (n=86) receiving an FKGL score <7. There were significant correlations between the DISCERN scores and the quantity of words (P<.001) and sentences (P<.001) on the websites. Additionally, there was a significant relationship (P<.001) between the number of sentences and FKGL and FRE scores. While readability was found to be very good, indicating that the information is accessible to the public, the quality of Arabic halitosis websites was poor, reflecting a significant gap in providing reliable and comprehensive health information. This highlights the need for improving the availability of high-quality materials to ensure Arabic-speaking populations have access to reliable information about halitosis and its treatment options, tying quality and availability together as critical for effective health communication.

PRO-READ IR: Enhanced PROcedural Information READability for Patient-Centered Care in Interventional Radiology With Large Language Models

PurposeTo evaluate the extent to which Generative Pre-trained Transformer 4 (GPT-4) can educate patients by generating easily understandable information about the most common interventional radiology (IR) procedures. Materials and methodsWe reviewed 10 IR procedures and prepared prompts for GPT-4 to provide patient educational instructions about each procedure in layman’s terms. The instructions were then evaluated by four clinical physicians and nine nonclinical assessors to determine their clinical appropriateness, understandability, and clarity using a survey. A grade-level readability assessment was performed using validated metrics to evaluate accessibility to a wide patient population. The same procedures were also evaluated from the patient instructions available at radiologyinfo.org and compared with GPT-generated instructions utilizing a paired t test. ResultsEvaluation by four clinical physicians shows that nine GPT-generated instructions were fully appropriate, whereas arterial embolization instructions was somewhat appropriate. Evaluation by nine nonclinical assessors shows that paracentesis, dialysis catheter placement, thrombectomy, ultrasound-guided biopsy, and nephrostomy-tube instructions were rated excellent by 57% and good by 43%. The arterial embolization and biliary-drain instructions were rated excellent by 28.6% and good by 71.4%. In contrast, thoracentesis, port placement, and CT-guided biopsy instructions received 43% excellent, 43% good, and 14% fair. The readability assessment across all procedural instructions showed a better Flesch-Kincaid mean grade of GPT-4 instructions compared with radiologyinfo.org (7.8 ± 0.87 versus 9.6 ± 0.83; P = .007) indicating excellent readability at 7th- to 8th-grade level compared with 9th to 10th grade. Additionally there was a lower Gunning Fog mean index (10.4 ± 1.2 versus 12.7 ± 0.93; P = .006), and higher Flesch Reading Ease mean score (69.4 ± 4.8 versus 51.3±3.9; P = .0001) indicating better readability. ConclusionIR procedural instructions generated by GPT-4 can aid in improving health literacy and patient-centered care in IR by generating easily understandable explanations.

Artificial Intelligence-Generated Patient Education Materials for Helicobacter pylori Infection: A Comparative Analysis.

Patient education contributes to improve public awareness of Helicobacter pylori. Large language models (LLMs) offer opportunities to revolutionize patient education transformatively. This study aimed to assess the quality of patient educational materials (PEMs) generated by LLMs and compared with physician sourced. Unified instruction about composing a PEM about H. pylori at a sixth-grade reading level in both English and Chinese were given to physician and five LLMs (Bing Copilot, Claude 3 Opus, Gemini Pro, ChatGPT-4, and ERNIE Bot 4.0). The assessments of the completeness and comprehensibility of the Chinese PEMs were conducted by five gastroenterologists and 50 patients according to three-point Likert scale. Gastroenterologists were asked to evaluate both English and Chinese PEMs and determine the accuracy and safety. The accuracy was assessed by six-point Likert scale. The minimum acceptable scores were 4, 2, and 2 for accuracy, completeness, and comprehensibility, respectively. The Flesch-Kincaid and Simple Measure of Gobbledygook scoring systems were employed as readability assessment tools. Accuracy and comprehensibility were acceptable for English PEMs of all sources, while completence was not satisfactory. Physician-sourced PEM had the highest accuracy mean score of 5.60 and LLM-generated English PEMs ranged from 4.00 to 5.40. The completeness score was comparable between physician-sourced PEM and LLM-generated PEMs in English. Chinese PEMs from LLMs proned to have lower score in accuracy and completeness assessment than English PEMs. The mean score for completeness of five LLM-generated Chinese PEMs was 1.82-2.70 in patients' perspective, which was higher than gastroenterologists' assessment. Comprehensibility was satisfactory for all PEMs. No PEM met the recommended sixth-grade reading level. LLMs have potential in assisting patient education. The accuracy and comprehensibility of LLM-generated PEMs were acceptable, but further optimization on improving completeness and accounting for a variety of linguistic contexts are essential for enhancing the feasibility.

A Content and Readability Analysis of Genitourinary and Sexual Health-Related Patient-Reported Outcome Measures in Gender-Affirming Care

ObjectivesTo the evaluate the readability and comprehensiveness of genitourinary and sexual health-related patient-reported outcomes (PROMs) used in gender-affirming care. MethodsCommon PROMs that measure genitourinary and sexual health-related outcomes in gender-affirming care literature were identified from six recent systematic reviews. Readability analysis was completed at the level of individual items and full scale using established readability assessment tool, including Flesch Kincaid Grade Level (FKGL), Gunning Fog Score (GF), Coleman Liau Index (CLI), and Simple Measure of Gobbledygook (SMOG) Index. The concepts measured by the PROMs were evaluated for comprehensiveness. Results25 PROMs were included, of which 12 assessed genitourinary outcomes and 13 assessed sexual health outcomes. A total of seven genitourinary domains and eight sexual health domains were identified during concept mapping. Readability analysis showed a median PROM grade level of 9.0 and 9.5 in genitourinary and sexual PROMs, respectively. The Patient-Reported Outcomes Measurement Information System Sexual Function and Satisfaction v2.0 had the lowest median reading grade level of 5.7, and the Female Sexual Function Index has the highest median reading grade level of 13.9. No single PROM was found to be comprehensive. Multiple PROMs contained double-barreled items or used outdated terminology. ConclusionsMost PROMs used in the genital gender-affirming literature failed to meet the readability recommendations for patient-facing material and were culturally unfit for use in transgender and gender-diverse individuals. None of the PROMs were found to be comprehensive for evaluating outcomes of gender-affirming care. Validated gender-affirming care-specific PROMs that are comprehensible, comprehensive, and relevant are urgently needed.

Can AI Answer My Questions? Utilizing Artificial Intelligence in the Perioperative Assessment for Abdominoplasty Patients.

Abdominoplasty is a common operation, used for a range of cosmetic and functional issues, often in the context of divarication of recti, significant weight loss, and after pregnancy. Despite this, patient-surgeon communication gaps can hinder informed decision-making. The integration of large language models (LLMs) in healthcare offers potential for enhancing patient information. This study evaluated the feasibility of using LLMs for answering perioperative queries. This study assessed the efficacy of four leading LLMs-OpenAI's ChatGPT-3.5, Anthropic's Claude, Google's Gemini, and Bing's CoPilot-using fifteen unique prompts. All outputs were evaluated using the Flesch-Kincaid, Flesch Reading Ease score, and Coleman-Liau index for readability assessment. The DISCERN score and a Likert scale were utilized to evaluate quality. Scores were assigned by two plastic surgical residents and then reviewed and discussed until a consensus was reached by five plastic surgeon specialists. ChatGPT-3.5 required the highest level for comprehension, followed by Gemini, Claude, then CoPilot. Claude provided the most appropriate and actionable advice. In terms of patient-friendliness, CoPilot outperformed the rest, enhancing engagement and information comprehensiveness. ChatGPT-3.5 and Gemini offered adequate, though unremarkable, advice, employing more professional language. CoPilot uniquely included visual aids and was the only model to use hyperlinks, although they were not very helpful and acceptable, and it faced limitations in responding to certain queries. ChatGPT-3.5, Gemini, Claude, and Bing's CoPilot showcased differences in readability and reliability. LLMs offer unique advantages for patient care but require careful selection. Future research should integrate LLM strengths and address weaknesses for optimal patient education. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Assessing the readability, reliability, and quality of artificial intelligence chatbot responses to the 100 most searched queries about cardiopulmonary resuscitation: An observational study.

This study aimed to evaluate the readability, reliability, and quality of responses by 4 selected artificial intelligence (AI)-based large language model (LLM) chatbots to questions related to cardiopulmonary resuscitation (CPR). This was a cross-sectional study. Responses to the 100 most frequently asked questions about CPR by 4 selected chatbots (ChatGPT-3.5 [Open AI], Google Bard [Google AI], Google Gemini [Google AI], and Perplexity [Perplexity AI]) were analyzed for readability, reliability, and quality. The chatbots were asked the following question: "What are the 100 most frequently asked questions about cardio pulmonary resuscitation?" in English. Each of the 100 queries derived from the responses was individually posed to the 4 chatbots. The 400 responses or patient education materials (PEM) from the chatbots were assessed for quality and reliability using the modified DISCERN Questionnaire, Journal of the American Medical Association and Global Quality Score. Readability assessment utilized 2 different calculators, which computed readability scores independently using metrics such as Flesch Reading Ease Score, Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook, Gunning Fog Readability and Automated Readability Index. Analyzed 100 responses from each of the 4 chatbots. When the readability values of the median results obtained from Calculators 1 and 2 were compared with the 6th-grade reading level, there was a highly significant difference between the groups (P < .001). Compared to all formulas, the readability level of the responses was above 6th grade. It can be seen that the order of readability from easy to difficult is Bard, Perplexity, Gemini, and ChatGPT-3.5. The readability of the text content provided by all 4 chatbots was found to be above the 6th-grade level. We believe that enhancing the quality, reliability, and readability of PEMs will lead to easier understanding by readers and more accurate performance of CPR. So, patients who receive bystander CPR may experience an increased likelihood of survival.

Assessment of the Arabic patient-centered online information about orthodontic pain: A quality and readability assessment.

This study assesses the quality and readability of Arabic online information about orthodontic pain. With the increasing reliance on the internet for health information, especially among Arabic speakers, it's critical to ensure the accuracy and comprehensiveness of available content. Our methodology involved a systematic search using the Arabic term for (Orthodontic Pain) in Google, Bing, and Yahoo. This search yielded 193,856 results, from which 74 websites were selected based on predefined criteria, excluding duplicates, scientific papers, and non-Arabic content. For quality assessment, we used the DISCERN instrument, the Journal of the American Medical Association (JAMA) benchmarks, and the Health on the Net (HON) code. Readability was evaluated using the Simplified Measure of Gobbledygook (SMOG), Flesch Reading Ease Score (FRES), and Flesch-Kincaid Grade Level (FKGL) scores. Results indicated that none of the websites received the HONcode seal. The DISCERN assessment showed median total scores of 14.96 (± 5.65), with low overall quality ratings. In JAMA benchmarks, currency was the most achieved aspect, observed in 45 websites (60.81%), but none met all four criteria simultaneously. Readability scores suggested that the content was generally understandable, with a median FKGL score of 6.98 and a median SMOG score of 3.98, indicating middle school-level readability. This study reveals a significant gap in the quality of Arabic online resources on orthodontic pain, highlighting the need for improved standards and reliability. Most websites failed to meet established quality criteria, underscoring the necessity for more accurate and trustworthy health information for Arabic-speaking patients.

Readability and quality assessment of online patient education materials for spinal and epidural anesthesia.

Guidelines recommend that health-related information for patients should be written at or below the sixth-grade level. We sought to evaluate the readability level and quality of online patient education materials regarding epidural and spinal anesthesia. We evaluated webpages with content written specifically regarding either spinal or epidural anesthesia, identified using 11 relevant search terms, with seven commonly used readability formulas: Flesh-Kincaid Grade Level (FKGL), Gunning Fox Index (GFI), Coleman-Liau Index (CLI), Automated Readability Index (ARI), Simple Measure of Gobbledygook (SMOG), Flesch Reading Ease (FRE), and New Dale-Chall (NDC). Two evaluators assessed the quality of the reading materials using the Brief DISCERN tool. We analyzed 261 webpages. The mean (standard deviation) readability scores were: FKGL = 8.8 (1.9), GFI = 11.2 (2.2), CLI = 10.3 (1.9), ARI = 8.1 (2.2), SMOG = 11.6 (1.6), FRE = 55.7 (10.8), and NDC = 5.4 (1.0). The mean grade level was higher than the recommended sixth-grade level when calculated with six of the seven readability formulas. The average Brief DISCERN score was 16.0. Readability levels of online patient education materials pertaining to epidural and spinal anesthesia are higher than recommended. When we evaluated the quality of the information using a validated tool, the materials were found to be just below the threshold of what is considered good quality. Authors of educational materials should provide not only readable but also good-quality information to enhance patient understanding.

Comparative Readability Assessment of Four Large Language Models in Answers to Common Contraception Questions [ID 2683638

INTRODUCTION: Large language models (LLMs) have the potential to revolutionize reproductive health education by offering accessible contraception counseling. The CDC, NIH, and AMA recommend presenting patient educational materials at or below a sixth-grade reading level. In this study, we present a comparative analysis of the readability of responses generated by LLMs, including OpenAI's ChatGPT 4.0, OpenAI's ChatGPT 3.5, Google Bard, and Microsoft Bing, to common contraception questions. METHODS: Referencing common contraception questions outlined in a recent review, we presented GPT-4.0, GPT-3.5, Google Bard, and Microsoft Bing with six common contraception questions on June 10, 2023, ensuring no prior chat bias. The readability of LLM outputs was evaluated using Flesch–Kincaid Grade Level (FK), Gunning Fog (GF), Automated Readability Index (ARI), and Coleman–Liau (CL) indices. All index score outputs correspond to a reading grade level (RGL), eg, RGL of 6 represents a sixth-grade level. RESULTS: Of the four LLMs, Google Bard demonstrated the highest readability with an average RGL of 10.6 (GF 10.1, FK 9.6, ARI 11.4, CL 11.4), followed by GPT-3.5 at an average RGL of 13.6 (GF 13.3, FK 12.6, ARI 15.2, CL 16.5). Microsoft Bing and GPT-4.0 yielded responses with higher average RGLs of 14.2 and 15.4, respectively. CONCLUSION: Google Bard and GPT-3.5 offered the most readable responses to contraception questions; however, these models still provided responses significantly above the recommended sixth-grade reading level (RGL of 6). Large language models should undergo evaluation to ensure alignment with established readability standards to optimize patient comprehension in reproductive counseling applications.

Quality and Readability Assessment of Web-based Arabic Health Information on Early Childhood Caries.

Patients are increasingly using the internet for searching health-related information. However, the quality and readability of the information available on the internet need to be assessed. To date, no study has assessed the quality and readability of web-based Arabic health information on early childhood caries. To evaluate the quality and readability of patient-oriented online Arabic health information regarding early childhood caries. For this infodemiological study, the Google and Yahoo search engines were searched using specific Arabic terms for early childhood caries, and the top 100 searches from both search engines were considered. Eligible websites were categorized in terms of affiliation as commercial, health portal, dental practice, professional, and journalism. The quality of the websites was assessed using the QUality Evaluation Scoring Tool (QUEST), and readability using the Gunning Fog index (GFI). A total of 140 websites were included after applying the exclusion criteria, of which 50.7% websites were of journalism. The majority of the websites (70%) had an overall low-quality level, with a QUEST score <10. The quality of websites retrieved from Google searches was of significantly higher quality than those from Yahoo (P < 0.0001). More than half (51.4%) of the websites had good readability, with a GFI score ≤8. Journalism websites had a significantly higher proportion of websites with poor readability level (62%) compared with other affiliations (P = 0.0072). The web-based Arabic information regarding early childhood caries is currently of low quality and moderate readability level, thereby indicating a need for improving such patient-facing content.

SEGO BORANAN IN BUYING AND SELLING CONTEXT OF MATHEMATICS PROBLEM SOLVING

This study aims to develop problem based learning in ethnomathematics-based textbooks through Sego Boranan’s buying and selling activities to improve students' problem-solving abilities in social arithmetic material. Research development were carried out a procedure of 3D (Define, Design, Develop). The feasibility assessment was carried out by experts and practitioners using the feasibility sheet, the readability assessment by students who had obtained Social Arithmetic material using readability sheets, and student responses by students who were studying Social Arithmetic material using a student response questionnaire. The textbooks developed were included in the very feasible criteria, was easy to understand, and very feasible to be implemented in learning mathematics. Although it is suggested that using an experimental method can test the effectiveness of using this textbook on learning outcomes and students' problem-solving abilities, this paper presented examples of ethnomathematics problem solving that can be practically implemented in mathematics classrooms.

Increasing Learning Results with Interactive Digital Materials PjBL Assisted by Heyzine Flipbook Maker

The internet has not been optimally utilized to support student learning, especially chemistry. Therefore, a research study was conducted to develop interactive digital teaching materials as an interactive e-module supported by the Heyzine application on colloids. This study aimed to develop teaching materials that possess (a) validity, (b) practicality, and (c) effectiveness. The research employed a Research and Development (R&amp;D) approach with a 4D development model (Define, Design, Develop, &amp; Disseminate). The developed e-module was tested on grade XI MIPA 2 students at SMA Negeri 10 Banjarmasin during the 2022/2023 academic year. The testing was conducted individually, in small groups, and a limited manner. Data was collected using questionnaires for validation, readability, response, and test assessment. Descriptive analysis was used to analyze the data. The results showed that the developed e-module met the validity, practicality, and effectiveness criteria. The validity test results indicated a high level of validity, with an average score of 95.64%. The e-module was also found to be practical based on the readability test, student and teacher response questionnaires, and the observation of teacher's abilities and implementation of learning. The practicality test scores indicated good to excellent quality. Furthermore, the limited testing demonstrated the effectiveness of the e-module in achieving learning objectives, as indicated by a high average n-gain score of 0.74. Overall, the developed e-module was able to enhance students' knowledge and learning outcomes in the field of colloids.

InterpretARA: Enhancing Hybrid Automatic Readability Assessment with Linguistic Feature Interpreter and Contrastive Learning

The hybrid automatic readability assessment (ARA) models that combine deep and linguistic features have recently received rising attention due to their impressive performance. However, the utilization of linguistic features is not fully realized, as ARA models frequently concentrate excessively on numerical values of these features, neglecting valuable structural information embedded within them. This leads to limited contribution of linguistic features in these hybrid ARA models, and in some cases, it may even result in counterproductive outcomes. In this paper, we propose a novel hybrid ARA model named InterpretARA through introducing a linguistic interpreter to better comprehend the structural information contained in linguistic features, and leveraging the contrastive learning that enables the model to understand relative difficulty relationships among texts and thus enhances deep representations. Both document-level and segment-level deep representations are extracted and used for the readability assessment. A series of experiments are conducted over four English corpora and one Chinese corpus to demonstrate the effectiveness of the proposed model. Experimental results show that InterpretARA outperforms state-of-the-art models in most corpora, and the introduced linguistic interpreter can provide more useful information than existing ways for ARA.

Is it easy to understand breast cancer treatment information on WeChat public accounts? Readability assessment on text and non-text factors

Is it easy to understand breast cancer treatment information on WeChat public accounts? Readability assessment on text and non-text factors

Exploring Automatic Readability Assessment for Science Documents within a Multilingual Educational Context

AbstractCurrent student-centred, multilingual, active teaching methodologies require that teachers have continuous access to texts that are adequate in terms of topic and language competence. However, the task of finding appropriate materials is arduous and time consuming for teachers. To build on automatic readability assessment research that could help to assist teachers, we explore the performance of natural language processing approaches when dealing with educational science documents for secondary education. Currently, readability assessment is mainly explored in English. In this work we extend our research to Basque and Spanish together with English by compiling context-specific corpora and then testing the performance of feature-based machine-learning and deep learning models. Based on the evaluation of our results, we find that our models do not generalize well although deep learning models obtain better accuracy and F1 in all configurations. Further research in this area is still necessary to determine reliable characteristics of training corpora and model parameters to ensure generalizability.

(021) ChatGPT's Ability to Assess Quality and Readability of Online Medical Information

Abstract Introduction Health literacy plays a crucial role in enabling patients to understand and effectively use medical information. As technology rapidly advances, the significance of health literacy becomes even more pronounced, particularly in comprehending complex medical information. Artificial Intelligence (AI) platforms have garnered significant attention for their remarkable ability to generate automated responses to a wide range of prompts. However, their capacity to assess the quality and readability of provided text remains uncertain. Given the growing prominence of AI web assistant tools, we hypothesized that integrating these tools into patients' web searches could enhance the retrieval of accurate medical information. Objective To evaluate the proficiency of Conversational Generative Pre-Trained Transformer (ChatGPT) in assessing readability, and utilizing the DISCERN tool to assess quality of online content regarding shock wave therapy for erectile dysfunction. Methods Websites were generated using a Google search of “shock wave therapy for erectile dysfunction” with location filters disabled. Readability was analyzed using the Readable software (Readable.com, Horsham, United Kingdom). Quality was assessed independently by three reviewers using the DISCERN tool. The same plain text files collected were inputted into ChatGPT to determine whether it produced comparable metrics for readability and quality. Results The study results revealed a notable disparity between ChatGPT's readability assessment and that obtained from a reliable tool, Readable.com (p&amp;lt;0.05). This indicates a lack of alignment between ChatGPT's algorithm and that of established tools, such as Readable.com. Similarly, the DISCERN score generated by ChatGPT differed significantly from the scores generated manually by human evaluators (p&amp;lt;0.05), suggesting that ChatGPT may not be capable of accurately identifying poor-quality information sources regarding shock wave therapy as a treatment for erectile dysfunction. Conclusions ChatGPT’s evaluation of the quality and readability of online text regarding shockwave therapy for erectile dysfunction differs from that of human raters and trusted tools. ChatGPT's current capabilities were not sufficient for reliably assessing the quality and readability of textual content. Further research is needed to elucidate the role of AI in the objective evaluation of online medical content in other fields. Continued development in AI and incorporation of tools such as DISCERN into AI software may enhance the way patients navigate the web in search of high-quality medical content in the future. Disclosure No.

Study to evaluate the readability and visual appearance of online resources for blunt chest trauma: an evaluation of online resources using mixed methods

ObjectivesBlunt chest trauma (BCT) is characterised by forceful and non-penetrative impact to the chest region. Increased access to the internet has led to online healthcare resources becoming used by the...

Ostomy Information on the Internet-Is It Good Enough?

The aim of this study was to determine which internet search engines and keywords patients with ostomies utilize, to identify the common websites using these terms, to determine what aspects of information they wanted, and to perform a quality and readability assessment for these websites. A cross-sectional survey of persons with ostomies to identify search engines and terms, followed by a structured assessment of the quality and readability of the identified web pages. The sample comprised 20 hospitalized patients with ostomies cared for on a colorectal surgical ward of a tertiary care hospital located in Melbourne, Australia. There were 15 (75%) adult males and 5 (25%) adult females; their mean age was 52.2years. Participants were surveyed between August and December 2020. Patients with newly formed ostomies were surveyed about which search engines and keywords they would use to look for information and for which questions regarding ostomies they wanted answers. In addition, 2 researchers then performed independent searches using the search terms identified by patient participants. These searches were conducted in August 2021, with the geographical location set to Australia. The quality of the websites was graded using the DISCERN, Ensuring Quality Information for Patients, and Quality Evaluation Scoring Tool scoring assessments, and their readability was graded using the Flesch Reading Ease Score tool. Participants used Google as their primary search engine. Four keywords/phrases were identified: stoma for bowel surgery, ileostomy, colostomy, and caring for stoma. Multiple web pages were identified, 8 (21%) originated from Australia, 7 (18%) were from the United Kingdom, and 23 (61%) were from the United States. Most web pages lacked recent updates; only 18% had been undated within the last 12months. The overall quality of the online information on ostomies was moderate with an average level of readability, deemed suitable for patient educational purposes. Information for persons living with an ostomy can be obtained from multiple web pages, and many sites have reasonable quality and are written at a suitable level. Unfortunately, these websites are rarely up-to-date and may contain advice that may not be applicable to individual patients.

Readability Assessment of Chinese Linguistic Texts Based on Dependent Syntactic Networks

Abstract Based on Chinese linguistic text features, this paper classifies dependent syntactic networks into two types: supervised and unsupervised, and researches to illustrate the inter-conversion relationship and difference between the two. Based on traditional linguistic features, the orthogonal features in linguistic features are fused with the neural network features extracted from the pre-trained model utilizing feature projection to complete the construction of the Chinese linguistics text readability assessment model, and the Chinese linguistics text readability assessment model is empirically analyzed. The results show that the accuracy of the six classification algorithms takes the range of 0.379-0.648 when only the baseline model is used, which is much lower than the performance of the corresponding classification models on the feature set constructed in this study, confirming that the algorithms in this paper can better fulfill the task of automatically assessing the readability of Chinese linguistics texts. The research results in this paper can be applied to educational scenarios to help teachers select reading materials of appropriate difficulty for learners.