Flesch-Kincaid Score Research Articles

Large language models (LLMs) for inferring genomic characteristics and facilitating genomic literacy in prostate cancer (PCa) patients.

261 Background: Clinicians use next-generation sequencing (NGS) to tailor targeted treatments for PCa patients. However, the results are often available in unstructured formats and are saturated with technical jargon, making them inaccessible for patients. Therefore, we aimed to assess the efficacy of LLMs to accurately extract genomic characteristics from unstructured reports and generate patient-friendly summaries for ease of variant interpretation in PCa patients. Methods: This retrospective study included patients with PCa who underwent NGS from 2023-2024. The GPT4 model was utilized using structured zero-shot prompts to extract genomic characteristics from unstructured reports in a non-decomposed manner. Prompt development was conducted iteratively using a 2% random sample of the dataset. Genomic characteristics ascertained by LLM were assessed against held out vendor-curated test dataset for evaluation. Performance was assessed using weighted evaluation metrics (precision, recall, and F1-score). Additionally, the extracted variables were organized using rule-based criterion and presented as a separate prompt to GPT4 to generate summaries for interpretation at the level of each biologically relevant variant. Mean Flesch-Kincaid scores and Vocd-D statistic with standard deviations were computed to assess readability and lexical diversity of LLM-generated variant interpretation summaries. Results: A total of 331 patients (370 NGS reports) were included in the study. Evaluation on the held-out test dataset (Table) showed that the weighted average precision ranged from 80% for extracting tumor mutational burden to 100% for extracting the CtDNA fraction from unstructured reports. The weighted average recall ranged from 85% for extracting CtDNA tumor fraction to 99% for ascertaining variant/alteration. The weighted average F1-scores varied from 0.83 for extracting tumor mutational burden to 1.0 for extracting specimen site from NGS reports. The mean Flesch-Kincaid score was 8.29 ± 1 indicating average/standard readability ease. The mean Vocd-D statistic was 7.55 ± 1 indicating focused summaries, however, with limited lexical diversity. Conclusions: This study suggests that large language models can effectively extract genomic characteristics from unstructured reports and can potentially improve genomic literacy among prostate cancer patients by providing easy-to-interpret variant summaries. However, targeted prompting may be required to increase lexical diversity for improved engagement. Performance across different genomic characteristics. Category Precision Recall F1-Score Altered gene 0.86 0.97 0.89 Variant/alteration type 0.98 0.99 0.98 Variant allele fraction 0.96 0.96 0.96 Test name/type 1 0.99 1 Specimen site 1 0.99 1 Tumor mutational burden 0.8 0.93 0.83 CtDNA tumor fraction 1 0.85 0.92 MSI status 0.99 0.97 0.98

Direct-to-consumer semen analysis products: Content, accountability, and adherence to clinical guidelines.

Direct-to-consumer (DTC) semen analysis (SA) products obviate barriers that deter men from clinic testing and have made strides in providing higher quality data. However, it is unclear how well these products adhere to the 2021 WHO guidelines on examination and processing of human spermatozoa as they pertain to the evaluation of male fertility. We investigate the content and adherence to clinical guidelines associated with consumer-facing information on DTC analysis products. Google search terms "at-home sperm test" and "mail-in sperm test" were used to identify commercially available products. Products that were offered to providers or were designed for use after vasectomy were excluded. We determined which semen parameters each product offered; if it was validated in subfertile men and against an established semen analysis method; and if it provided guidance in accordance with the WHO 2021 guidelines and related AUA/ASRM guidelines. The accountability and esthetic appearance of content for each product were assessed using the Silberg accountability and modified Abbott's criteria, respectively. Readability was assessed using the Flesch-Kincaid score. Descriptive statistics and two-tailed t-tests were used to compare characteristics, with p<0.05 as statistically significant. We identified 20 at-home (n=17) and mail-in (n=3) products. 85% of tests reported sperm concentration, with 60% and 15% of tests reporting motility and morphology, respectively. Fewer than half recommended men perform more than one test (40%), and none highlighted the need for partner evaluation. Only 25% were validated among subfertile men. Most tests lacked information on their credentials and sponsors, though were esthetically pleasing. Consumer-facing information on average required a high school level education for comprehension. DTC products inconsistently provide clinical recommendations in accordance with guidelines. Additionally, while product content is on average aesthetically appealing, it may lack accountability. Formal recommendations on the content and use of such products may improve their clinical integration.

Modern Digital Query Analytics of Patient Education Materials on Acanthosis Nigricans: Systematic Search and Content Analysis.

Online digital materials are integral to patient education and health care outcomes in dermatology. Acanthosis nigricans (AN) is a common condition, often associated with underlying diseases such as insulin resistance. Patients frequently search the internet for information related to this cutaneous finding. To our knowledge, the quality of online educational materials for AN has not been systematically examined. The primary objective of this study was to profile the readability and quality of the content of publicly available digital educational materials on AN and identify questions frequently asked by patients. This study analyzed publicly available internet sources to identify the most frequent questions searched by patients regarding AN using the Google Rankbrain algorithm. Furthermore, available articles on AN were evaluated for quality and reading level using metrics such as the Brief DISCERN score, and readability was determined using three specific scales including the Flesch-Kincaid score, Gunning Fog index, and the Coleman-Liau index, based on literature. Patients most frequently accessed facts on AN from government sources, which comprised 30% (n=15) of the analyzed sources. The available articles did not meet quality standards and were at a reading level not appropriate for the general public. The majority of articles (n=29/50, 58%) had substandard Brief DISCERN scores, failing to meet the criteria for good quality. Clinicians should be aware of the paucity of valuable online educational material on AN and educate their patients accordingly.

Accuracy of natural language processors for patients seeking inguinal hernia information.

NLPs such as ChatGPT are novel sources of online healthcare information that are readily accessible and integrated into internet search tools. The accuracy of NLP-generated responses to health information questions is unknown. We queried four NLPs (ChatGPT 3.5 and 4, Bard, and Claude 2.0) for responses to simulated patient questions about inguinal hernias and their management. Responses were graded on a Likert scale (1 poor to 5 excellent) for relevance, completeness, and accuracy. Responses were compiled and scored collectively for readability using the Flesch-Kincaid score and for educational quality using the DISCERN instrument, a validated tool for evaluating patient information materials. Responses were also compared to two gold-standard educational materials provided by SAGES and the ACS. Evaluations were performed by six hernia surgeons. The average NLP response scores for relevance, completeness, and accuracy were 4.76 (95% CI 4.70-4.80), 4.11 (95% CI 4.02-4.20), and 4.14 (95% CI 4.03-4.24), respectively. ChatGPT4 received higher accuracy scores (mean 4.43 [95% CI 4.37-4.50]) than Bard (mean 4.06 [95% CI 3.88-4.26]) and Claude 2.0 (mean 3.85 [95% CI 3.63-4.08]). The ACS document received the best scores for reading ease (55.2) and grade level (9.2); however, none of the documents achieved the readibility thresholds recommended by the American Medical Association. The ACS document also received the highest DISCERN score of 63.5 (57.0-70.1), and this was significantly higher compared to ChatGPT 4 (50.8 [95% CI 46.2-55.4]) and Claude 2.0 (48 [95% CI 41.6-54.4]). The evaluated NLPs provided relevant responses of reasonable accuracy to questions about inguinal hernia. Compiled NLP responses received relatively low readability and DISCERN scores, although results may improve as NLPs evolve or with adjustments in question wording. As surgical patients expand their use of NLPs for healthcare information, surgeons should be aware of the benefits and limitations of NLPs as patient education tools.

Improving readability and comprehension levels of otolaryngology patient education materials using ChatGPT

Improving readability and comprehension levels of otolaryngology patient education materials using ChatGPT

Quality of Information About Kidney Stones from Artificial Intelligence Chatbots.

Introduction: Kidney stones are common and morbid conditions in the general population with a rising incidence globally. Previous studies show substantial limitations of online sources of information regarding prevention and treatment. The objective of this study was to examine the quality of information on kidney stones from artificial intelligence (AI) chatbots. Methods: The most common online searches about kidney stones from Google Trends and headers from the National Institute of Diabetes and Digestive and Kidney Diseases website were used as inputs to four AI chatbots (ChatGPT version 3.5, Perplexity, Chat Sonic, and Bing AI). Validated instruments were used to assess the quality (DISCERN instrument from 1 low to 5 high), understandability, and actionability (PEMAT, from 0% to 100%) of the chatbot outputs. In addition, we examined the reading level of the information and whether there was misinformation compared with guidelines (5 point Likert scale). Results: AI chatbots generally provided high-quality consumer health information (median DISCERN 4 out of 5) and did not include misinformation (median 1 out of 5). The median understandability was moderate (median 69.6%), and actionability was moderate to poor (median 40%). Responses were presented at an advanced reading level (11th grade; median Flesch-Kincaid score 11.3). Conclusions: AI chatbots provide generally accurate information on kidney stones and lack misinformation; however, it is not easily actionable and is presented above the recommended reading level for consumer health information.

Identifying ChatGPT-Written Patient Education Materials Using Text Analysis and Readability.

Artificial intelligence (AI)-based text generators such as Chat Generative Pre-Trained Transformer (ChatGPT) have come into the forefront of modern medicine. Given the similarity between AI-generated and human-composed text, tools need to be developed to quickly differentiate the two. Previous work has shown that simple grammatical analysis can reliably differentiate AI-generated text from human-written text. In this study, ChatGPT was used to generate 25 articles related to obstetric topics similar to those made by the American College of Obstetrics and Gynecology (ACOG). All articles were geared towards patient education. These AI-generated articles were then analyzed for their readability and grammar using validated scoring systems and compared to real articles from ACOG. Characteristics of the 25 AI-generated articles included fewer overall characters than original articles (mean 3,066 vs. 7,426; p < 0.0001), a greater average word length (mean 5.3 vs. 4.8; p < 0.0001), and a lower Flesch-Kincaid score (mean 46 vs. 59; p < 0.0001). With this knowledge, a new scoring system was develop to score articles based on their Flesch-Kincaid readability score, number of total characters, and average word length. This novel scoring system was tested on 17 new AI-generated articles related to obstetrics and 7 articles from ACOG, and was able to differentiate between AI-generated articles and human-written articles with a sensitivity of 94.1% and specificity of 100% (Area Under the Curve [AUC] 0.99). As ChatGPT is more widely integrated into medicine, it will be important for health care stakeholders to have tools to separate originally written documents from those generated by AI. While more robust analyses may be required to determine the authenticity of articles written by complex AI technology in the future, simple grammatical analysis can accurately characterize current AI-generated texts with a high degree of sensitivity and specificity. · More tools are needed to identify AI-generated text in obstetrics, for both doctors and patients.. · Grammatical analysis is quick and easily done.. · Grammatical analysis is a feasible and accurate way to identify AI-generated text..

Assessment of the quality of online patient information resources for patients considering parastomal hernia treatment.

The aim was to examine the quality of online patient information resources for patients considering parastomal hernia treatment. A Google search was conducted using lay search terms for patient facing sources on parastomal hernia. The quality of the content was assessed using the validated DISCERN instrument. Readability of written content was established using the Flesch-Kincaid score. Sources were also assessed against the essential content and process standards from the National Institute for Health and Care Excellence (NICE) framework for shared decision making support tools. Content analysis was also undertaken to explore what the sources covered and to identify any commonalities across the content. Fourteen sources were identified and assessed using the identified tools. The mean Flesch-Kincaid reading ease score was 43.61, suggesting that the information was difficult to read. The overall quality of the identified sources was low based on the pooled analysis of the DISCERN and Flesch-Kincaid scores, and when assessed against the criteria in the NICE standards framework for shared decision making tools. Content analysis identified eight categories encompassing 59 codes, which highlighted considerable variation between sources. The current information available to patients considering parastomal hernia treatment is of low quality and often does not contain enough information on treatment options for patients to be able to make an informed decision about the best treatment for them. There is a need for high-quality information, ideally co-produced with patients, to provide patients with the necessary information to allow them to make informed decisions about their treatment options when faced with a symptomatic parastomal hernia.

ChatGPT-4 and Human Researchers Are Equal in Writing Scientific Introduction Sections: A Blinded, Randomized, Non-inferiority Controlled Study.

Background Natural language processing models are increasingly used in scientific research, and their ability to perform various tasks in the research process is rapidly advancing. This study aims to investigate whether Generative Pre-trained Transformer 4 (GPT-4) is equal to humans in writing introduction sections for scientific articles. Methods This randomized non-inferiority study was reported according to the Consolidated Standards of Reporting Trials for non-inferiority trials and artificial intelligence (AI) guidelines. GPT-4 was instructed to synthesize 18 introduction sections based on the aim of previously published studies, and these sections were compared to the human-written introductions already published in a medical journal. Eight blinded assessors randomly evaluated the introduction sections using 1-10 Likert scales. Results There was no significant difference between GPT-4 and human introductions regarding publishability and content quality. GPT-4 had one point significantly better scores in readability, which was considered a non-relevant difference. The majority of assessors (59%) preferred GPT-4, while 33% preferred human-written introductions. Based on Lix and Flesch-Kincaid scores, GPT-4 introductions were 10 and two points higher, respectively, indicating that the sentences were longer and had longer words. Conclusion GPT-4 was found to be equal to humans in writing introductions regarding publishability, readability, and content quality. The majority of assessors preferred GPT-4 introductions and less than half could determine which were written by GPT-4 or humans. These findings suggest that GPT-4 can be a useful tool for writing introduction sections, and further studies should evaluate its ability to write other parts of scientific articles.

Comparison of Patient Education Materials Generated by Chat Generative Pre-Trained Transformer Versus Experts: An Innovative Way to Increase Readability of Patient Education Materials.

Improving patient education materials may improve patient outcomes. This study aims to explore the possibility of generating patient education materials with the assistance of a large language model, Chat Generative Pre-Trained Transformer (ChatGPT). In addition, we compare the accuracy and readability of ChatGPT-generated materials versus expert-generated materials. Patient education materials in implant-based breast reconstruction were generated by experts and ChatGPT independently. Readability and accuracy of the materials are the main outcomes. Readability of the materials was compared using Flesch-Kincaid score. Accuracy of the materials generated by ChatGPT was evaluated by 2 independent reviewers. Content errors are categorized into information errors, statistical errors, and multiple errors (errors more than 2 types). The content generated by experts had higher readability. The Flesch-Kincaid score is at the 7.5 grade for expert-generated materials, whereas the content generated by ChatGPT is at the 10.5 grade (despite ChatGPT being asked to generate content at the seventh grade level). The accuracy of ChatGPT-generated content is 50%, with most errors being information errors. ChatGPT often provides information about breast reduction or breast augmentation, despite being asked specifically about breast reconstruction. Despite its limitation, ChatGPT significantly reduced the time required to generate patient education materials. Although it takes experts 1 month to generate patient education materials, ChatGPT generates materials within 30 minutes. ChatGPT can be a powerful starting tool to generate patient education materials. However, its readability and accuracy still require improvements.

Exploring the Intersection of Artificial Intelligence and Neurosurgery: Let us be Cautious With ChatGPT.

ChatGPT is a novel natural language processing artificial intelligence (AI) module where users enter any question or command and receive a single text response within seconds. As AI becomes more accessible, patients may begin to use it as a resource for medical information and advice. This is the first study to assess the neurosurgical information that is provided by ChatGPT. ChatGPT was accessed in January 2023, and prompts were created requesting treatment information for 40 common neurosurgical conditions. Quantitative characteristics were collected, and four independent reviewers evaluated the responses using the DISCERN tool. Prompts were compared against the American Association of Neurological Surgeons (AANS) "For Patients" webpages. ChatGPT returned text organized in paragraph and bullet-point lists. ChatGPT responses were shorter (mean 270.1 ± 41.9 words; AANS webpage 1634.5 ± 891.3 words) but more difficult to read (mean Flesch-Kincaid score 32.4 ± 6.7; AANS webpage 37.1 ± 7.0). ChatGPT output was found to be of "fair" quality (mean DISCERN score 44.2 ± 4.1) and significantly inferior to the "good" overall quality of the AANS patient website (57.7 ± 4.4). ChatGPT was poor in providing references/resources and describing treatment risks. ChatGPT provided 177 references, of which 68.9% were inaccurate and 33.9% were completely falsified. ChatGPT is an adaptive resource for neurosurgical information but has shortcomings that limit the quality of its responses, including poor readability, lack of references, and failure to fully describe treatment options. Hence, patients and providers should remain wary of the provided content. As ChatGPT or other AI search algorithms continue to improve, they may become a reliable alternative for medical information.

Readability assessment of patient educational materials for pediatric spinal conditions from top academic orthopedic institutions.

The Internet has become a popular source of health information for patients and their families. Healthcare experts recommend that the readability of online education materials be at or below a sixth grade reading level. This translates to a standardized Flesch Reading Ease Score between 81 and 90, which is equivalent to conversational English. However, previous studies have demonstrated that the readability of online education materials of various orthopedic topics is too advanced for the average patient. To date, the readability of online education materials for pediatric spinal conditions has not been analyzed. The objective of this study was to assess the readability of online educational materials of top pediatric orthopedic hospital websites for pediatric spinal conditions. Online patient education materials from the top 25 pediatric orthopedic institutions, as ranked by the U.S. News and World Report hospitals for pediatric orthopedics, were assessed utilizing multiple readability assessment metrics including Flesch-Kincaid, Flesch Reading Ease, Gunning Fog Index, and others. Correlations between academic institutional ranking, geographic location, and the use of concomitant multimedia modalities with Flesch-Kincaid scores were evaluated using a Spearman regression. Only 32% (8 of 25) of top pediatric orthopedic hospitals provided online health information at or below a sixth grade reading level. The mean Flesch-Kincaid score was 9.3 ± 2.5, Flesch Reading Ease 48.3 ± 16.2, Gunning Fog Score 10.7 ± 3.0, Coleman-Liau Index 12.1 ± 2.8, Simple Measure of the Gobbledygook Index 11.7 ± 2.1, Automated Readability Index 9.0 ± 2.7, FORCAST 11.3 ± 1.2, and Dale-Chall Readability Index 6.7 ± 1.4. There was no significant correlation between institutional ranking, geographic location, or use of video material with Flesch-Kincaid scores (p = 0.1042, p = 0.7776, p = 0.3275, respectively). Online educational material for pediatric spinal conditions from top pediatric orthopedic institutional websites is associated with excessively complex language which may limit comprehension for the majority of the US population. Economic and Decision Analysis/level III.

Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning-Based Text Simplification Approach.

In most cases, the abstracts of articles in the medical domain are publicly available. Although these are accessible by everyone, they are hard to comprehend for a wider audience due to the complex medical vocabulary. Thus, simplifying these complex abstracts is essential to make medical research accessible to the general public. This study aims to develop a deep learning-based text simplification (TS) approach that converts complex medical text into a simpler version while maintaining the quality of the generated text. A TS approach using reinforcement learning and transformer-based language models was developed. Relevance reward, Flesch-Kincaid reward, and lexical simplicity reward were optimized to help simplify jargon-dense complex medical paragraphs to their simpler versions while retaining the quality of the text. The model was trained using 3568 complex-simple medical paragraphs and evaluated on 480 paragraphs via the help of automated metrics and human annotation. The proposed method outperformed previous baselines on Flesch-Kincaid scores (11.84) and achieved comparable performance with other baselines when measured using ROUGE-1 (0.39), ROUGE-2 (0.11), and SARI scores (0.40). Manual evaluation showed that percentage agreement between human annotators was more than 70% when factors such as fluency, coherence, and adequacy were considered. A unique medical TS approach is successfully developed that leverages reinforcement learning and accurately simplifies complex medical paragraphs, thereby increasing their readability. The proposed TS approach can be applied to automatically generate simplified text for complex medical text data, which would enhance the accessibility of biomedical research to a wider audience.

Readability assessment of patient educational materials for pediatric spinal deformity from top academic orthopedic institutions

Study designCross-sectional analysis of patient educational materials from top pediatric orthopedic hospital websites.ObjectiveTo assess the readability of online educational materials of top pediatric orthopedic hospital websites for pediatric spinal deformity.Summary of background dataThe internet has become an increasingly popular source of health information for patients and their families. Healthcare experts recommend that the readability of online education materials be at or below a 6th-grade reading level. However, previous studies have demonstrated that the readability of online education materials on various orthopedic topics is too advanced for the average patient. To date, the readability of online education materials for pediatric spinal deformity has not been analyzed.MethodsOnline patient education materials from the top 25 pediatric orthopedic institutions, as ranked by the U.S. News and World Report hospitals for pediatric orthopedics, were accessed utilizing the following readability assessments: Flesch–Kincaid (FK), Flesch Reading Ease, Gunning Fog Index, Coleman–Liau Index, Simple Measure of the Gobbledygook Index (SMOG), Automated Readability Index, FORCAST, and the New Dale and Chall Readability. Correlations between academic institutional ranking, geographic location, and the use of concomitant multi-media modalities with FK scores were evaluated using a Spearman regression.ResultsOnly 48% (12 of 25) of top pediatric orthopedic hospitals provided online information regarding pediatric spinal deformity at or below a 6th-grade reading level. The mean FK score was 9.0 ± 2.7, Flesch Reading Ease 50.8 ± 15.6, Gunning Fog Score 10.6 ± 3.1, Coleman–Liau Index 11.6 ± 2.6, SMOG index 11.7 ± 2.0, Automated Readability Index 8.6 ± 2.8, and Dale–Chall Readability Score 6.4 ± 1.4. There was no significant correlation between institutional ranking, geographic location, or use of multimedia with FK scores.ConclusionOnline educational material for pediatric spinal deformity from top pediatric orthopedic institutional websites are associated with poor readability.

Online Patient Education Materials Related to Lipoprotein(a): Readability Assessment.

BackgroundLipoprotein(a) (Lp(a)) is a highly proatherogenic lipid fraction that is a clinically significant risk modifier. Patients wanting to learn more about Lp(a) are likely to use online patient educational materials (OPEMs). However, the readability of OPEMs may exceed the health literacy of the public.ObjectiveThis study aims to assess the readability of OPEMs related to Lp(a). We hypothesized that the readability of these online materials would exceed the sixth grade level recommended by the American Medical Association.MethodsUsing an online search engine, we queried the top 20 search results from 10 commonly used Lp(a)-related search terms to identify a total of 200 websites. We excluded duplicate websites, advertised results, research journal articles, or non–patient-directed materials, such as those intended only for health professionals or researchers. Grade level readability was calculated using 5 standard readability metrics (automated readability index, SMOG index, Coleman-Liau index, Gunning Fog score, Flesch-Kincaid score) to produce robust point (mean) and interval (CI) estimates of readability. Generalized estimating equations were used to model grade level readability by each search term, with the 5 readability scores nested within each OPEM.ResultsA total of 27 unique websites were identified for analysis. The average readability score for the aggregated results was a 12.2 (95% CI 10.9798-13.3978) grade level. OPEMs were grouped into 6 categories by primary source: industry, lay press, research foundation and nonprofit organizations, university or government, clinic, and other. The most readable category was OPEMs published by universities or government agencies (9.0, 95% CI 6.8-11.3). The least readable OPEMs on average were the ones published by the lay press (13.0, 95% CI 11.2-14.8). All categories exceeded the sixth grade reading level recommended by the American Medical Association.ConclusionsLack of access to readable OPEMs may disproportionately affect patients with low health literacy. Ensuring that online content is understandable by broad audiences is a necessary component of increasing the impact of novel therapeutics and recommendations regarding Lp(a).

Readability, stability, and internal consistency of a new psychometric inventory on evidence-based practice in physiotherapy

Evidence-based practice (EBP) has, in the last decade, gained global prominence in health care professions because it provides the framework for lifelong and self-directed learning. These traits are crucial for the continued provision of quality health care. This study sets out to develop a culturally appropriate instrument to measure physiotherapists' knowledge, beliefs, attitudes, and behaviors relative to the use of EBP and establish the instruments' psychometric properties. A 53-item EBP inventory that consisted of seven parts sociodemographic, EBP competence and behaviors, perceived knowledge of EBP, perceived skills and resources, attitudes about EBP, and barriers related to the use of EBP was created. Theinstrument was administered to 25 physiotherapists within a two-week interval on two occasions. The Flesch-Kincaid Reading Ease and Flesch-Kincaid scores for the instrument were 49.5 and 8.3, respectively. Its Cronbach alpha range from "fair" (0.333, p&lt;.001) to "almost perfect" (0.837, p&lt;.001). The test-retest (stability) scores for the instrument parts were significantly (?2 = 4.738, p&lt;.038) different for only one (competence on EBP) of the seven factors. The overall findings revealed the instrument is relatively easy to comprehend, highly stable, and internally consistent. The availability of this instrument will promote further studies of EBP in physiotherapy.

Readability assessment of patient educational materials for shoulder arthroplasty from top academic orthopedic institutions.

Readability assessment of patient educational materials for shoulder arthroplasty from top academic orthopedic institutions.

Co-designing a Lifestyle-Focused Text Message Intervention for Women After Breast Cancer Treatment: Mixed Methods Study.

BackgroundBreast cancer is the most common cancer among women globally. Recovery from breast cancer treatment can be mentally and physically challenging. SMS text message programs offer a novel way to provide health information and support, but few programs are co-designed with consumer representatives.ObjectiveThis study aims to report the procedures and outcomes of a co-design process of a lifestyle-focused SMS text message program to support women’s mental and physical health after breast cancer treatment.MethodsWe followed an iterative mixed methods two-step process: (1) co-design workshop with consumers and health professionals and researchers to draft text messages and (2) evaluation of message content, which was scored (5-point Likert scale; 1=strongly disagree to 5=strongly agree) for ease of understanding, usefulness, and appropriateness, and readability (Flesch-Kincaid score). Additional free-text responses and semistructured interviews were coded into themes. Messages were edited or deleted based on the evaluations, with consumers’ evaluations prioritized.ResultsIn step 1, co-designed text messages (N=189) were semipersonalized, and the main content themes were (1) physical activity and healthy eating, (2) medications and side effects, (3) mental health, and (4) general breast cancer information. In step 2, consumers (n=14) and health professionals and researchers (n=14) provided 870 reviews of 189 messages and found that most messages were easy to understand (799/870, 91.8%), useful (746/870, 85.7%), and appropriate (732/870, 84.1%). However, consumers rated 50 messages differently from health professionals and researchers. On the basis of evaluations, 37.6% (71/189) of messages were deleted, 36.5% (69/189) were edited, and 12 new messages related to fatigue, self-care, and cognition were created. The final 130 text messages had a mean 7.12 (SD 2.8) Flesch-Kincaid grade level and 68.9 (SD 15.5) ease-of-reading score, which represents standard reading ease.ConclusionsCo-designing and evaluating a bank of evidence-based mental and physical health-themed text messages with breast cancer survivors, health professionals, and researchers was feasible and resulted in a bank of 130 text messages evaluated highly by participants. Some consumer evaluations differed from health professionals and researchers, supporting the importance of co-design.

Intelligence Perception from Text: Accuracy and Cues

The present study deals with the first impression about a person’s intelligence, based solely on their text. We attempted to calculate the accuracy of a reader’s judgment, and to determine which features of the text are associated with both the reader’s impression as well as the author’s intelligence test scores (Brunswik lens model). The study involved 116 participants aged 16 to 23 years. Each of them read and evaluated 4 out of 20 texts that were written by other participants in an earlier study. Participants evaluated both the general impression of the author’s intelligence and of the text itself, scoring responses to questions such as “How smart is the author of this text?” and to other text features that may be associated with intelligence judgement, including “vocabulary richness” and “grammar complexity”. In addition, objective indicators such as readability (Flesch-Kincaid score), length, and relative errors were calculated for the texts. The accuracy of reader judgments, estimated in a text-based analysis as the Spearman correlation coefficient between the “how smart is the author” score and the actual author test scores, did not reach statistical significance. However, the data indicate the presence of a nonlinear relationship. As revealed by a participant-based analysis, authors from the lower quartile (based on test scores) tend to be rated lower by participants as well. Readability, vocabulary richness, grammar complexity, and the relative number of words longer than six letters all were utilized by the reader participants in their judgments (i.e., correlated with impression scores) and at the same time were valid (i.e., correlated with test scores). We discuss the problem of the narrowness of scientific and psychological impressions on intelligence, compared to everyday perceptions

Design, Assessment, and Validation of a Questionnaire to Estimate Food-Dependent Exercise-Induced Anaphylaxis Prevalence in Latin American Population.

There are no epidemiological data about food-dependent exercise-induced anaphylaxis (FDEIA) in Latin America. Our aim was to design, assess, and validate a questionnaire to identify potential FDEIA cases and/or estimate its prevalence by self-report. Questions were included in the instrument to address the main symptoms of FDEIA, type/intensity of physical activity, and anaphylaxis. The instrument’s clarity, comprehension and repeatability were evaluated. These evaluations were carried out by Hispanic people (Argentinians/Colombians/Mexicans/Peruvians), including nine individuals with medical diagnosis of FDEIA, and Brazilians. The Flesch–Kincaid score was calculated using the INFLESZ software. The instrument was translated from Spanish to Brazilian Portuguese following the translation back-translation procedure. The participants rated the two versions of the questionnaire as clear and comprehensible (three-point ordinal scale) and very easy to understand [0.33; average (scale 0–10)]. For these evaluations, the Kendall’s W coefficient showed strong agreement among raters (W = 0.80; average). The Flesch–Kincaid score was 63.5 in average (documents considered as readable). The Cohen’s Kappa coefficient showed almost perfect agreement in repeatability (0.88; average). The validation process of two versions of an instrument, used to identify potential FDEIA cases, was successfully carried out and it was found applicable to Latin American countries for generating epidemiological data.