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Few risk-forecasting models of allergic rhinitis (AR) exist that may aid AR pre-exposure prophylaxis (PrEP) in clinical practice. Therefore, this study aimed to develop and validate an effective clinical model for identifying candidates for AR PrEP using a routine medical questionnaire. This study was conducted in 10 Chinese provinces with 13 medical centers (n = 877) between 2019 and 2021. Clinical characteristics and exposure history were collected via face-to-face interviews. Well-trained physicians diagnosed patients with AR based on skin prick test results and clinical performance. The least absolute shrinkage and selection operator model was used to identify potential risk factors for AR, and the logistic regression model was used to construct the risk-forecasting model. Predictive power and model reliability were assessed using area under the receiver operating characteristic curve and calibration curves, respectively. This study diagnosed 625 patients with AR who had positive responses to at least one indoor or outdoor allergen and 460 to at least one outdoor pollen allergen. Two nomograms were established to identify two types of AR with various sensitization patterns. Both models had an area under curve of approximately 0.7 in the development and internal validation datasets. Additionally, our findings found good agreement for the calibration curves of both models. Early identification of candidates for AR PrEP using routine medical information may improve the deployment of limited resources and effective health management. Our models showed good performance in predicting AR; therefore, they can serve as potential automatic screening tools to identify AR PrEP candidates. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion Patient or Public Contribution Implications for Clinical practice Developing a severity assessment scale for critically ill patients' thirst and conducting reliability and validity tests, aiming to provide healthcare professionals with a scientific and objective tool for assessing the level of thirst.Based on literature review and qualitative interviews, a pool of items was generated, and a preliminary scale was formed through two rounds of Delphi expert consultation. Convenience sampling was employed to select 178 ICU patients in a top‐three hospital from May 2023 to October 2023 as the study subjects to examine the reliability and validity of the severity assessment scale for critically ill patients' thirst.The developed severity assessment scale for critically ill patients' thirst consists of 8 evaluation items and 26 evaluation indicators. The agreement coefficients for two rounds of expert consultation were 100% and 92.6% for the positive coefficient, and the authority coefficients were .900 and .906. Kendall's concordance coefficients were .101 and .120 (all p < .001). The overall Cronbach's α coefficient for the scale was .827. The inter‐rater reliability coefficient was .910. The Item‐Content Validity Index (I‐CVI) ranged from .800 to 1.000, and the Scale‐Content Validity Index/Average (S‐CVI/Ave) was .950.The critically ill patients' thirst assessment scale is reliable and valid and can be widely used in clinical practice.The AiMi Academic Services (www.aimieditor.com) for English language editing and review services.The scale developed in this study is a simple and ICU‐specific scale that can be used to assess the severity of thirst in critically ill patients. As such, the severity of thirst in critically ill patients can be evaluated quickly so that targeted interventions can be implemented according to the patient's specific disease and treatment conditions. Therefore, patient comfort can be improved, and thirst‐related health problems can be prevented. [ABSTRACT FROM AUTHOR]