Text Pages Research Articles

Readability and Accessibility of Patient-Education Materials for Heart Failure in the United States

Online education materials are widely used by patients and caregivers to understand the management of complex chronic diseases such as heart failure (HF). Organizations such as the American Medical Association and National Institutes of Health recommend that materials be written at a 6th grade reading level. The current study examined the readability and accessibility of online education materials for patients with HF. Whole page text from each included website was entered into an online readability calculator. Five validated readability indices (Flesch-Kincaid Grade Level, Flesch Reading Ease Scale, Gunning Fog Index, Coleman-Liau Index, and Simple Measure of Gobbledygook (SMOG Index)) were used to evaluate each source. Websites were categorized by source (government, public, and private). The availability of audiovisual accessibility features and content in non-English languages were assessed for each website. Of the 36 online resources analyzed, the median readability level was 9-10th grade by the Flesch-Kincaid Grade Level and college level using the Flesch Reading Ease Scale. The Gunning Fog Index and Coleman-Liau Index both showed median readability scores corresponding to a 12th grade reading level, while the SMOG Index showed a median score corresponding to that of the 9th grade. Only 10 websites (28%) offered information in languages other than English, and none provided comprehensive accessibility features for users with disabilities. Common online educational materials for patients with HF are characterized by a higher readability level than that recommended by the National Institutes of Health and American Medical Association with limited multilingual and accessibility options, potentially limiting the accessibility of resources to patients and caregivers.

Characteristics and outcomes of prehospital and emergency department surgical airways.

The surgical airway is a high acuity, low occurrence procedure. Data on the complications and outcomes of surgical airways are limited. Our primary objective was to describe immediate complications, late complications, and clinical outcomes of patients who underwent a surgical airway procedure in the prehospital or emergency department (ED) setting. We conducted a retrospective chart review of patients ≥14 years at an academic medical center who underwent a surgical airway procedure in the ED, the prehospital setting, or at a referring ED prior to interfacility transfer. We identified cases from keyword searches of prehospital text pages and hospital electronic medical records from June 1, 2008 to July 1, 2022. Manual chart review was used to confirm inclusion and determine patient and procedure characteristics. Outcomes included immediate complications, delayed in-hospital complications, and neurologic disability as defined by Modified Rankin Score (mRS) at discharge. We identified 63 patients (34 prehospital, 11 ED, and 18 referring ED). Immediate complications included mainstem intubation (46.0%) and bleeding that required direct pressure (23.4%). Overall, 29 patients (46%) died after arrival to the hospital. Of the patients surviving to hospital admission, 25 (48%) had an airway-related complication. Nine complications were deemed directly related to technical components of the procedure. Of the patients who survived to discharge, 18 (52.9%) had poor neurologic function (mRS 4-5). Procedural complications, mortality, and poor neurologic function were common following a surgical airway procedure in the prehospital or ED setting. Most patients surviving to discharge had a moderate to severe neurologic disability.

Automatic Topic-Based Web Page Classification Using Deep Learning

The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.

1257. The Impact of Communication Type on Acceptance Rates of Antimicrobial Stewardship Interventions

Abstract Background While face-to-face communication (ie. “handshake stewardship” (HS)) has been shown to improve antimicrobial stewardship program (ASP) intervention acceptance rates, this strategy is resource intensive. Therefore, systems unable to conduct HS must identify the optimal mode of communication for ASP recommendations. This study evaluated the effects of communication type on ASP intervention acceptance. Methods An infectious disease physician and ASP pharmacist met Monday through Friday to assess appropriateness of select antimicrobials. ASP recommendations were delivered through direct communication via telephone conversations or passive communication via text-page. A text page consisted of a message limited to 240 characters sent directly to the primary team. We performed a retrospective review of all interventions and associated acceptance rates, antimicrobials and antimicrobial indications between January 1st, 2023 to March 31st, 2023. The primary outcome evaluated was ASP intervention acceptance rate. Results Recommendations were delivered through telephone conversations in 267/454 (58.8%) cases compared to 187/454 (41.2%) cases in which recommendations were provided via text-page. We found that direct communication delivered telephonically resulted in a significant increase in ASP intervention acceptance rate compared to passive communication via text page (82.8% vs 73.8%, respectively; p=0.02). This resulted in 884 and 552 days of antimicrobial therapy saved in the telephonic and text-page group, respectively (p=0.64). Significant differences in baseline characteristics included: antimicrobial indications of intraabdominal, respiratory, sepsis and febrile neutropenia; having a broad differential; and an intervention category of de-escalation. Conclusion Direct communication with clinicians (eg. telephonically) improves ASP intervention acceptance rates compared to passive communication styles (eg. text-page). Direct communication with the prescriber encourages shared decision making, increases visibility of ASP, and allows for education to modify prescribing behavior. The success of an ASP relies heavily on provider relationships, which direct communication can further promote. Disclosures All Authors: No reported disclosures

Are Text Pages an Effective Nudge to Increase Attendance at Internal Medicine Morning Report Conferences? A Cluster Randomized Controlled Trial.

Despite the importance of medical educational conferences, low attendance remains an issue. The utility of reminder text pages as a behavioral nudge to increase attendance is unknown. Our objective was to determine whether reminder text pages increase daily morning report attendance. We conducted a multiple-crossover cluster randomized controlled trial among medical students and internal medicine interns and residents (learners) at the Veteran Affairs Boston Healthcare System during the 2019 to 2020 academic year. During intervention periods, all residents and interns received a text page reminder 5 minutes before the upcoming 8:00 am morning report conference; no page was sent during control periods. The primary outcome was conference attendance 10 minutes after the start of the conference. The study period included 85 morning report conferences, which 211 unique learners were eligible to attend; outcome data were available for 100% of eligible learners. On days when no page was sent, 44.4% of eligible learners attended the conference by 8:10 am ; on days when a reminder page was sent, 49.5% of eligible learners attended (P = .007). Accounting for clustering within individuals and controlling for date and team, the adjusted risk difference in morning report attendance associated with a reminder page was 4.0% (95% CI, 0.5%-7.6%) compared with no reminder page. No effect modification by overnight admissions was detected. Our results suggest that daily reminder pages may result in a small increase in conference attendance. Whether this small increase is educationally significant will vary across training programs that apply this strategy.

A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy

Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the category of web pages based on the text content of the web pages. Due to the unavailability of a matrix representation that can handle long web page text content, this study employs a document representation known as word cloud image to visualise the words extracted from the text content web page after data pre-processing. The most popular words are shown in large size and appear in the centre of the word cloud image. The most common words are the words that appear frequently in the text content web page and are related to describing what the web page content is about. The Convolutional Neural Network (CNN) recognises the pattern of words presented in the core portions of the word cloud image to categorise the category to which the web page belongs. The proposed model for web page classification has been compared with the other web page classification models. It shows the good result that achieved an accuracy of 85.6%. It can be used as a tool that helps to make identifying the category of web pages more accurate

Using Retrieved Sources for Semantic and Lexical Plagiarism Detection

Plagiarism is described as using someone else's ideas or work without their permission. Using lexical and semantic text similarity notions, this paper presents a plagiarism detection system for examining suspicious texts against available sources on the Web. The user can upload suspicious files in pdf or docx formats. The system will search three popular search engines for the source text (Google, Bing, and Yahoo) and try to identify the top five results for each search engine on the first retrieved page. The corpus is made up of the downloaded files and scraped web page text of the search engines' results. The corpus text and suspicious documents will then be encoded as vectors. For lexical plagiarism detection, the system will leverage Jaccard similarity and Term Frequency-Inverse Document Frequency (TFIDF) techniques, while for semantic plagiarism detection, Doc2Vec and Sentence Bidirectional Encoder Representations from Transformers (SBERT) intelligent text representation models will be used. Following that, the system compares the suspicious text to the corpus text. Finally, a generated plagiarism report will show the total plagiarism ratio, the plagiarism ratio from each source, and other details.

Mentorship and Scientific Writing

Mentorship and Scientific Writing

Mentorship and Scientific Writing

Mentorship and Scientific Writing

Fractal algorithms and RGB image processing in scribal and ink identification on an 1819 secret initiation manuscript to the “Philike Hetaereia”

Historical texts incorporate important characteristics that need to be assessed including genre, text structure and content. Often overlooked are characteristics of handwritten manuscripts commonly divided into legibility, readability and aesthetics. To determine the scientific feasibility of classification of handwritten texts an objective approach is developed to describe twenty handwritten pages of an 1819 Greek manuscript, that refers to the initiation to the Greek secret “friendly society” (Philike Hetaereia) organization, established as part of the Greek independence against the Ottoman Turks. It is investigated through a fractal and RGB image analysis approach. Fractal Minkowski Dimension was applied on the handwritten text and the RGB color analysis on the ink and paper and both were used as a non-invasive manner and revealed interesting results. The novel RGB image analysis and the fractal analysis of the manuscript identified respectively, five iron gall inks and four scribes from the ink content and handwritten styles, of the compact five lines text and whole text pages. The novel approach was verified with another old manuscript of known ink pigments, as well as with thirteen known handwritten texts of that period and four prints representing modern and similar period texts substantiating the findings of the novel methods.

Sanctity

In this narrative medicine essay, an obstetrician finds relief from the frenetic intrusions of text messages, pages, and emails when in the operating room, where all devices are turned off and all her attention is turned on to her patient.

IMPLEMENTATION OF AUTOMATIC ABSTRACTING OF WEB PAGE CONTENT

The problem of developing a brief description of the text of a web page is investigated. The brief description was created by using the Transformer machine learning architecture, which in a pre-trained state allows you to summarize the text. In the course of implementation, the model of generating text abstraction is investigated. The possibilities of the model are investigated, due to which features it is able to process and annotate text. The results of the model are analyzed and compared with the results of the work of other referencing models, which explains the choice of the studied model.

A Topic-Specific Web Crawler using Deep Convolutional Networks

This paper presented a new focused crawler that efficiently supports the Turkish language. The developed architecture was divided into multiple units: a control unit, crawler unit, link extractor unit, link sorter unit, and natural language processing unit. The crawler's units can work in parallel to process the massive amount of published websites. Also, the proposed Convolutional Neural Network (CNN) based natural language processing unit can professionally classifying Turkish text and web pages. Extensive experiments using three datasets have been performed to illustrate the performance of the developed approach. The first dataset contains 50,000 Turkish web pages downloaded by the developed crawler, while the other two are publicly available and consist of “28,567” and “22,431” Turkish web pages, respectively. In addition, the Vector Space Model (VSM) in general and word embedding state-of-the-art techniques, in particular, were investigated to find the most suitable one for the Turkish language. Overall, results indicated that the developed approach had achieved good performance, robustness, and stability when processing the Turkish language. Also, Bidirectional Encoder Representations from Transformer (BERT) was found to be the most appropriate embedding for building an efficient Turkish language classification system. Finally, our experiments showed superior performance of the developed natural language processing unit against seven state-of-the-art CNN classification systems. Where accuracy improvement compared to the second-best is 10% and 47% compared to the lowest performance.

Omit Needless Words: Sentence Length Perception

Introduction: Most measures of text readability quantify sentence length. Short sentences typically promote readability, and therefore inclusiveness. Omitting needless words from one’s own writing entails skimming the text for long sentences (targets) among short sentences (distractors) -a visual search task followed by a numerosity task. Do these tasks depend on perceptually learned global text orientation? Method: Before collecting data I pre-registered the hypotheses, predictions, methods, and statistical power analysis on the Open Science Framework ( https://osf.io/3k5cn ). Subsequently, the web-participant service “Prolific” recruited 88 adults who reported English as their first language and completed the experiment online using the pavlovia.org platform. Each trial began with a 2-second presentation of a text page containing one bolded sentence among numerous non-bolded sentences. Across trials, the text appeared either in standard orientation (like the page you are reading now) or flipped (upside down or mirror reversed). Participants judged whether the bolded sentence contained more or fewer than 17 words. Results & Conclusion. The experiment generated four findings. First, naive participants showed excellent precision in judging sentence length: thresholds approximated 1.3-word fluctuations around a 17-word mean sentence length (Weber Fraction = ~7.4%). Second, the precision and speed of sentence length judgments remained virtually unchanged after flipping standard-orientation-text either vertically or horizontally. Third, error patterns indicated that participants estimated sentence length by counting the lines of text rather than by counting individual words. This "chunking" strategy has the advantage of bringing sentence-length estimates into the subitizing range: 1 to 3 lines versus 10 to 24 words. Fourth, interleaving standard text and upside down text had no effect on response biases. However, interleaving standard text and mirror-reversed text significantly biased participants toward underestimating sentence length in both conditions. The specificity of this underestimate provides evidence for a laterally anchored scene syntax in visual search (Wolfe, 2021).

Information Visualization Design of Web under the Background of Big Data

With the rapid development of the Internet, the information on the Internet presents an explosive growth. Cloud computing and big data analysis technology based on Internet information rise accordingly. However, all web pages contain not only important information but also the noise information irrelevant to the subject information. They seriously affect the accuracy of information extraction, so the research of web page information extraction technology arises at the historic moment and becomes the research hotspot. The quality of web page text information will directly affect the accuracy of later information processing and decision-making. If we can accurately evaluate the information of the web pages captured from the Internet and classify the extracted web pages according to the corresponding characteristics, we can not only improve the efficiency of information processing, but also improve the practical value of the information decision-making system. From the practical application requirements and user-friendly operation point of view, the information visualization of web design based on big data is studied in this paper. Specifically, the system designed in this paper improves the traditional template-based web information extraction method, establishes a web information extraction rule scheme combined with templates, and achieves the goal of web information extraction rule selection and template generation in the visual environment. Finally, the visualization algorithm based on T-SNE verifies the effectiveness of the web page information visualization algorithm designed in this paper.

WEB PAGE CLASSIFICATION WITH DEEP LEARNING METHODS

Today, millions of websites on the Internet are widely used to access information. For effective use of web pages with increasing numbers every day, they need to be well classified. In this study, binary and multi-class classification models have been created which can classify web pages with high accuracy. In our experiments, URLs and categories of English web pages in the Open Directory Project (ODP) were used. Training dataset was created by pulling web page texts from URL information. To our knowledge, this is the first comprehensive web page classification dataset for Turkish. In this study, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning methods which are effective in text classification are used. Word embedding was used instead of n-gram approaches commonly used for feature extraction in text classification studies. In this study, hyper-parameter optimization was performed for deep learning models. Binary and multi-class classification models were created with the best parameters. Binary classification models were compared with the results of another study, and multi-class classification models were compared with each other. The performances of all models were examined by considering their training time and f1 scores.

Automatic Extraction of Web Page Text Information Based on Network Topology Coincidence Degree

In order to effectively solve the above problems, an automatic extraction method of web text information based on network topology coincidence degree is proposed. Search engine, web crawler, and hypertext tag are used to classify web text information, and then, dimensionality reduction is carried out. After processing, the similarity of different features of web page text information is calculated, the similarity is sorted, and the similar text information is extracted according to the correlation based on segment estimation. The experimental results show that the designed method can simplify the complexity of the associated information of the data set and improve the amount of data collection and the success rate of information collection.

Kesalahan Penerjemahan Google Translate Pada Hadis Arbain Berdasarkan Konteks Keagamaan (Kajian Akar Kata dan Morfologi Turunan)

Translation is the activity producing a source language message with the closest and natural equivalent to the recipient's language, both in terms of meaning and style. The translation process requires grammatical and lexical adjustments to evade unusual structures in the recipient's language. Translation activities are not only done by humans. With the advancement of technology, electronic translation tools have emerged such as Google Translate to translate parts of text or web pages from one language to another. This study aims to describe the Google Translate system in translating a Hadith Arbain text and to analyze the forms of translation errors in the study of root words and derived morphology. This research is a qualitative descriptive study with the source of data and the object is the translation of the Google translation and the Hadith Arbain. Data collection is done by documentation. Error analysis is used in conducting data analysis of translation errors to find errors in the meaning of Arabic morphology forms against Indonesian and vice versa, grammatical errors, meaning of the same root words but different sounds, meaning of glorification, classifying words of the original language into the target language, the use of inappropriate target words, translation, and the meaning of Islamic concepts.

To the Editor: Actions More Powerful Than Words-An Impactful Approach to Overcoming Page Burden.

I would like to thank Armendariz et al for their contribution to understanding the root characteristics of rounding interruptions inherent to medical learners in the inpatient teaching environment.1 As noted in the study conclusion, proactive communication with nursing staff holds merit for most team-based improvement strategies. However, based on anecdotal experience, simple discussions with nursing staff resulted in trivial long-term change. An effective, actionable plan is needed for residency programs to address inappropriately timed pages and improve paging etiquette during inpatient rounding.Inpatient medicine has trended toward algorithmic-based patient care under the banners of improved patient outcomes and hospital efficiency. Examples within this trend include antibiotic prescribing practices, pathology-directed power plans, and sepsis triage goals.2 Standardization for interprofessional communication, however, remains largely unchanged and differs across the spectrum of the US health care system. This leads to increased stress, burnout rates, and frustrations during rounding periods among medical trainees.3An algorithm-based text page system for nursing staff would improve communication and rounding efficiency by utilizing a decision tree model. This tree would include the initial decision of page appropriateness during rounding times. If it meets criteria for paging, the algorithm will require important triaging information for the text page, including urgency level (routine, urgent, emergent); patient room number; paging staff role and name; category (lab, clinical status change, medication question, disposition, diet request, family request, etc); and time requested for response (within 1 hour, 30 minutes, 5 minutes, or ASAP). Similar interventions in the pediatric ICU setting demonstrated a 42% reduction in pages per patient, improved resident well-being and nursing satisfaction, and maintained patient safety while using a text page system.4Implementing standardized, algorithm-based text page systems can improve the resident learning experience while decreasing page burden during inpatient rounds.

Spelling variation and text alignment

AbstractIn the 15th century, at a time when codification via dictionaries and grammars had not yet taken effect, printers, editors, and compositors were already producing pamphlets and books that had to meet the new requirements of the letterpress, especially as regards the arrangement of white space and uniform line justification (even-margined on the left and right). The following analysis investigates five German editions of theMirabilia Romae(Marvels of the City of Rome), a well-known pilgrim guide, all printed in 1500 for the contemporaneous Jubilee year and thus for short-term sale. The results show that compositors used different means for text alignment: In addition to deviations in line counts and the repositioning of lines, they chose extended or contracted spelling variants, predominantly on the second half of the page. The most frequent variants are abbreviations in the form of tildes. However, just a few spelling patterns with tildes were used. With respect to explanatory processes in a historical perspective, the results call for a closer consideration of page format, text layout (mise-en-page) and line justification when evaluating spelling variation in early book printing.