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Background: In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. Methods: We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. Results: A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. Conclusions: The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD. [ABSTRACT FROM AUTHOR]

Background: Thymic epithelial tumors (TETs) are rare malignant tumors with limited treatment options. No established second‐line treatment regimen is available following the preferred first‐line chemotherapy, resulting in unsatisfactory efficacy and poor prognosis for patients with advanced TETs. This study aimed to evaluate the efficacy of small molecule multitarget antiangiogenic inhibitors as well as the prognostic factors for advanced TETs. Methods: A retrospective study was conducted using data from a real‐world database. Clinical information and survival follow‐up data were collected from 52 patients with advanced TETs who received small molecule multitarget antiangiogenic inhibitors at Zhejiang Cancer Hospital between August 10, 2016 and August 10, 2022. The short‐term efficacy of the treatments, survival time of the patients, and relevant prognostic factors of advanced TETs were analyzed. Results: Out of the 52 patients included in this study, 16 had thymoma and 36 had thymic carcinoma. The 52 patients had an overall response rate of 21.1% and a disease control rate of 94.2%. In addition, the median progression‐free survival (PFS) was 8.05 months, and the overall survival (OS) was 25.00 months. Apatinib was given to 33 patients, anlotinib to 15 patients, and sunitinib or lenvatinib to four patients. Only seven patients received antiangiogenic inhibitors as their first‐line therapy, 27 patients as their second‐line therapy, and 18 patients as third‐line or subsequent therapy. Meanwhile, 42 patients received monotherapy with an antiangiogenesis inhibitor, while 10 patients received combination therapy. Univariate analysis indicated that the combined treatment was associated with a superior OS (p = 0.044); multivariate analysis indicated that the combined treatment was an independent prognostic factor for PFS (p = 0.014) and OS (p = 0.012). Conclusion: The findings suggest that small molecule multitarget antiangiogenic inhibitors are efficacious as second or post‐line treatments as a viable alternative treatment option for patients with advanced TETs. [ABSTRACT FROM AUTHOR]

Rhizoma coptidis (CR) is traditionally used for treating gastrointestinal diseases. Wine-processed CR (wCR), zingiber-processed CR (zCR), and evodia-processed CR (eCR) are its major processed products. However, the related study of their specific mechanisms is very limited, and they need to be further clarified. The aim of this study is to compare the intervening mechanism of wCR/zCR/eCR on rats via faecal metabolomics and 16S rDNA gene sequencing analysis. First, faecal samples were collected from the control and CR/wCR/zCR/eCR groups. Then, a metabolomics analysis was performed using UHPLC-Q/TOF-MS to obtain the metabolic profile and significantly altered metabolites. The 16S rDNA gene sequencing analysis was carried out to analyze the composition of gut microbiota and screen out the significantly altered microbiota at the genus level. Finally, a pathway enrichment analysis of the significantly altered metabolites via the KEGG database and a functional prediction of relevant gut microbes based on PICRUSt2 software were performed in combination. Together with the correlation analysis between metabolites and gut microbiota, the potential intervening mechanism of wCR/zCR/eCR was explored. The results suggested that wCR played a good role in maintaining immune homeostasis, promoting glycolysis, and reducing cholesterol; zCR had a better effect on protecting the integrity of the intestinal mucus barrier, preventing gastric ulcers, and reducing body cholesterol; eCR was good at protecting the integrity of the intestinal mucus barrier and promoting glycolysis. This study scientifically elucidated the intervening mechanism of wCR/zCR/eCR from the perspective of faecal metabolites and gut microbiota, providing a new insight into the processing mechanism research of Chinese herbs. [ABSTRACT FROM AUTHOR]

Objectives: To investigate public awareness of colorectal cancer (three components: total knowledge, confidence and anticipated delay) in the Chinese population, to explore factors associated with total knowledge and to elucidate relationships among three components of public awareness of colorectal cancer. Methods: We recruited 562 adult Chinese participants with no history of colorectal cancer between March and May 2020 by convenience sampling method. Data were collected online using a self‐designed demographic questionnaire and a simplified Chinese version of the Bowel Cancer Awareness Measure. Univariate analysis and multivariate linear regression were applied. Results: The mean score for total knowledge was 10.56 (SD: 5.89). Over half of the participants (58.2%) lacked confidence about detecting warning signs. For 42.7% of participants, the anticipated delay was not within the acceptable range (2 weeks). Totally eight demographic variables were identified as significant predictors of total knowledge, accounting for 36.2% of the variance. Total knowledge was positively correlated with confidence (r = 0.126, p < 0.01) and negatively associated with anticipated delay (F = 8.891, p < 0.01). Conclusion: Public awareness of colorectal cancer was low in the Chinese population. Hence, educational interventions targeted for improving knowledge, enhancing individuals' confidence in detecting symptoms and reducing barriers to seeking medical help may be urgently required. [ABSTRACT FROM AUTHOR]

Previous research has demonstrated that differential item functioning (DIF) methods that do not account for multilevel data structure could result in too frequent rejection of the null hypothesis (i.e., no DIF) when the intraclass correlation coefficient (ρ) of the studied item was the same as the ρ of the total score. The current study extended previous research by comparing the performance of DIF methods when ρ of the studied item was less than ρ of the total score, a condition that may be observed with considerable frequency in practice. The performance of two simple and frequently used DIF methods that do not account for multilevel data structure, the Mantel–Haenszel test (MH) and logistic regression (LR), was compared with the performance of a complex and less frequently used DIF method that does account for multilevel data structure, hierarchical logistic regression (HLR). Simulation indicated that HLR and LR performed equivalently in terms of significance tests under most conditions, and MH was conservative across most of the conditions. Effect size estimate of HLR was equally accurate and consistent as effect size estimates of LR and MH under the Rasch model and was more accurate and consistent than LR and MH effect size estimates under the two-parameter item response theory model. The results of the current study provide evidence to help researchers further understand the comparative performance between complex and simple modeling for DIF detection under multilevel data structure. [ABSTRACT FROM PUBLISHER]