OBJECTIVE: To assess the capabilities of large language models (LLMs), including Open AI (GPT-4.0) and Microsoft Bing (GPT-4), in generating structured reports, the Breast Imaging Reporting and Data System (BI-RADS) categories, and management recommendations from free-text breast ultrasound reports.
METHODS: In this retrospective study, 100 free-text breast ultrasound reports from patients who underwent surgery between January and May 2023 were gathered. The capabilities of Open AI (GPT-4.0) and Microsoft Bing (GPT-4) to convert these unstructured reports into structured ultrasound reports were studied. The quality of structured reports, BI-RADS categories, and management recommendations generated by GPT-4.0 and Bing were evaluated by senior radiologists based on the guidelines.
RESULTS: Open AI (GPT-4.0) was better than Microsoft Bing (GPT-4) in terms of performance in generating structured reports (88% vs. 55%; p < 0.001), giving correct BI-RADS categories (54% vs. 47%; p = 0.013) and providing reasonable management recommendations (81% vs. 63%; p < 0.001). As the ability to predict benign and malignant characteristics, GPT-4.0 performed significantly better than Bing (AUC, 0.9317 vs. 0.8177; p < 0.001), while both performed significantly inferior to senior radiologists (AUC, 0.9763; both p < 0.001).
CONCLUSIONS: This study highlights the potential of LLMs, specifically Open AI (GPT-4.0), in converting unstructured breast ultrasound reports into structured ones, offering accurate diagnoses and providing reasonable recommendations.

Double-blind, placebo-controlled, randomized controlled trials are the gold standard for clinical trials in nutrition science. For trials of whole diets, dietary counseling are advantageous as they offer clinical translatability although can vary in the fidelity of the intended intervention from participant to participant and across studies. Feeding trials, in which most or all food is provided, offer high precision and can provide proof-of-concept evidence that a dietary intervention is efficacious and can also better evaluate the effect of known quantities of foods and nutrients on physiology. However, they come with additional methodological complexities. Feeding trials also call for a variety of unique methodological considerations, not least of which relate to the design and delivery of diets to participants. This review aims to provide a comprehensive summary of recommendations for design and conduct of feeding trials, encompassing domiciled and non-domiciled feeding trials. Several pertinent aspects of trial design and methodology are discussed, including defining the study population to maximize retention, safety and generalisability of findings, recommendations for design of control interventions and optimising blinding, and specific considerations for clinical populations. A detailed stepwise process for menu design, development, validation and delivery are also presented. These recommendations aim to facilitate methodologic consistency and execution of high quality feeding trials, ultimately facilitating improved understanding of the role of diet in treating disease and the underpinning mechanisms.

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Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations.

Background and objective Although mental health is always a major concern, particularly for airline pilots, knowledge of and attitudes toward mental health have not always been emphasized for safe operations in the aviation industry. Fear of self-reporting, stigmas, and lack of knowledge about mental health conditions are prevalent in this industry. The purpose of our research was to examine pilots\' perceptions of mental health issues, the resources available to them, and the reasons they may or may not report these issues. Methods We conducted a qualitative, phenomenological study in which 21 commercial pilots were interviewed to better understand their perceptions of mental health issues, available self-help resources, and rationale for failing to report mental health issues. Results The results of our analysis using NVivo software showed that pilots neither reported the issues nor trusted the processes meant to address mental health issues. Three themes emerged from the research: (1) pilots avoid discussing mental health issues for fear of repercussions, (2) although resources exist, pilots generally distrust the confidentiality of reporting systems, and (3) pilots honestly believe that reporting any mental health issue will be devastating to their careers. Conclusions Airline companies and the Federal Aviation Administration (FAA) need to change processes and instill a sense of trust in reporting systems among pilots so that they feel safe reporting mental health concerns and receive improved treatment. This can lead to more accurate reporting of conditions and ensure safe flight operations.

OBJECTIVE: The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, first published in 2009, has been widely endorsed and compliance is high in systematic reviews of intervention studies. Systematic reviews of prevalence studies are increasing in frequency, but their characteristics and reporting quality have not been examined in large studies. Our objectives were to describe the characteristics of systematic reviews of prevalence studies in adults, evaluate the completeness of reporting and explore study-level characteristics associated with the completeness of reporting.
METHODS: We did a meta-research study. We searched 5 databases from January 2010 to December 2020 to identify systematic reviews of prevalence studies in adult populations. We used the PRISMA 2009 checklist to assess completeness of reporting and recorded additional characteristics. We conducted a descriptive analysis of review characteristics and linear regression to assess the relationship between compliance with PRISMA and publication characteristics.
RESULTS: We included 1172 systematic reviews of prevalence studies. The number of reviews increased from 25 in 2010 to 273 in 2020. The median PRISMA score for systematic reviews without meta-analysis was 17.5 out of a maximum of 23 and, for systematic reviews with meta-analysis, 22 out of a maximum of 25. Completeness of reporting, particularly for key items in the methods section was suboptimal. Systematic reviews that included a meta-analysis or reported using a reporting or conduct guideline were the factors most strongly associated with increased compliance with PRISMA 2009.
CONCLUSIONS: Reporting of systematic reviews of prevalence was adequate for many PRISMA items. Nonetheless, this study highlights aspects for which special attention is needed. Development of a specific tool to assess the risk of bias in prevalence studies and an extension to the PRISMA statement could improve the conduct and reporting of systematic reviews of prevalence studies.

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BACKGROUND: The healthcare adverse event (HAE) reporting framework is more than just a tool. It is a crucial pillar in our pursuit of patient safety, quality improvement, trust maintenance, regulatory compliance, and ethical standards in healthcare delivery.
OBJECTIVE: To assess healthcare workers\' satisfaction with the HAE reporting framework and the management approach towards such reporting in the emergency department of rural government hospitals by conducting a satisfaction survey.
METHODS: This prospective observational research was conducted in the Department of Emergency Medicine of the Uttar Pradesh University of Medical Sciences, Saifai, and adjoining district hospitals from November 2023 to January 2024. The study involved 320 healthcare professionals working in the emergency department. The quantitative survey research used a questionnaire and a quality Likert scale response. The data were analyzed on an ordinal measurement scale using nonparametric statistical methods. The sample data were analyzed using frequency tables, percentage pie charts, and comparison bar graphs. In nonparametric statistical tests, the one-sample Wilcoxon signed rank test was used to infer the population\'s central tendency, and the Kruskal-Wallis test was used to make inferences about the population categories.
RESULTS: The satisfaction of healthcare professionals with the HAE reporting framework and the management approach was diverse. When surveyed about the HAE reporting framework in the emergency department, out of the 320 healthcare professionals, 50% (161) expressed dissatisfaction, 47% (149) were satisfied, and 3% (10) did not comment. Paramedics were most dissatisfied (61% of 133). When asked about the management approach while dealing with such reporting, 50% (159) were satisfied, 46% (147) were unsatisfied, and 4% (14) did not comment. On comparing professions, 43% (29) of 33 doctors and 62% (83) of 133 paramedics were unsatisfied, giving a poor response. Additionally, 61% (72) of the 119 nursing staff were satisfied. The non-parametric inferential tests yielded a p-value of less than 0.001 for both questions, indicating a notable difference in the population\'s response to the HAE reporting framework and management approach. On pairwise comparison, there was a significant difference in perception (p<0.001) between the occupation groups, except for doctors and paramedics (p = 0.638) in the HAE reporting framework.
CONCLUSIONS: By encouraging reporting, standardizing processes, analyzing incidents thoroughly, and using data-driven insights to inform improvement efforts, healthcare organizations can enhance patient safety, improve quality of care, and prevent future adverse events. The management approach to HAE reporting involves fostering a culture of safety and transparency, implementing standardized reporting systems, providing education and training to healthcare staff, establishing feedback mechanisms, conducting robust analysis of reported events, promoting continuous improvement, and ensuring transparency and accountability.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is one of the most common cancers worldwide. A delay in the diagnosis of OSCC can have a drastic impact on management and patient outcomes. One of the most crucial elements in oral management is the timely histopathological final diagnosis. Turnaround time (TAT) is regarded as the most important component of the quality performance evaluation. Many labs have struggled to improve their TATs despite advancements in computerization, transport systems, and analytical technologies.  Aim: This study aimed to assess the TAT of OSCC cases, assess the mean TAT period, evaluate any TAT delays, and explore the reasons behind the TAT delays.  Materials and methods: OSCC reports in Saveetha Dental College and Hospitals, Chennai, for one year from January 1, 2022, to December 31, 2022, were retrieved from the Dental Information Archival Software (DIAS), and the mean TAT was noted. Further, the number of cases with delay in TAT was also observed, and the reason for their delay was listed. Descriptive statistics and graphical representation were performed utilizing IBM SPSS Statistics for Windows, V. 23.0 (IBM Corp., Armonk, NY, USA). One-way ANOVA was performed with a significance set at a p-value less than 0.05.
RESULTS: 230 OSCC cases were retrieved and included in the TAT evaluation for this study. Among 230 cases, 161 (70%) were incisional and 69 (30%) were excisional biopsies. Only seven (4%) incisional cases and seven (10%) excisional biopsies showed a delay in TAT. The most common reason for the delay in TAT was the requirement for deeper sections and decalcification of bone specimens. Out of 161 incisional cases, only 48 (29%) have undergone excision and further treatment. Twenty-one out of 69 (30%) excisional cases were found to be referral cases from other private institutions. The overall average TAT for 12 months was 3.24 ± 0.41 days for incisional biopsies and 11.88 ± 2.07 days for excisional biopsies. One-way ANOVA revealed a statistically significant p-value of less than 0.00001.
CONCLUSIONS: Our study sheds light on specific challenges in TAT delay and opportunities for the improvement of TAT. This can result in faster TAT of OSCC reports, further improve patient care, and enable prompt treatment. This study quantified the TAT for OSCC cases and identified critical areas for process improvement. The findings can inform strategies to streamline diagnostic workflows, reduce delays, and ultimately improve the timely delivery of care to patients with OSCC.

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