BACKGROUND: Guidelines must be interpreted comprehensively and correctly to standardize the clinical process. However, this process is challenging and requires interpreters to have a medical background and qualifications. In this study, the accuracy of ChatGPT3.5 in answering clinical questions related to the 2019 guidelines for severe acute pancreatitis was evaluated.
RESULTS: An observational study was conducted using the 2019 guidelines for severe acute pancreatitis. The study compared the accuracy of ChatGPT3.5 in English versus Chinese and found that it was more accurate in English (71%) than in Chinese (59%) (P value: 0.203). Additionally, the study assessed the accuracy of ChatGPT3.5 in answering short-answer questions versus true/false questions and found that it was more accurate in answering short-answer questions (76%) than in answering true/false questions (60%) (P value: 0.405).
CONCLUSIONS: For clinicians managing severe acute pancreatitis, ChatGPT3.5 may have potential value. However, it should not be relied upon excessively for clinical decision making.

BACKGROUND: Surgery and radiotherapy are primary nonconservative treatments for prostate cancer (PCa). However, personalizing treatment options between these treatment modalities is challenging due to unclear criteria. We developed an artificial intelligence (AI)-based model that can identify patients with localized PCa who would benefit more from either radiotherapy or surgery, thereby providing personalized clinical decision-making.
METHODS: Data from consecutive patients with localized PCa who received radiotherapy or surgery with complete records of clinicopathological variables and follow-up results in 12 registries of the Surveillance, Epidemiology, and End Results database were analyzed. Patients from 7 registries were randomly assigned to training (TD) and internal validation datasets (IVD) at a 9:1 ratio. The remaining 5 registries constituted the external validation dataset (EVD). TD was divided into training-radiotherapy (TRD) and training-surgery (TSD) datasets, and IVD was divided into internal-radiotherapy (IRD) and internal-surgery (ISD) datasets. Six models for radiotherapy and surgery were trained using TRD and TSD to predict radiotherapy survival probability (RSP) and surgery survival probability (SSP), respectively. The models with the highest concordance index (C-index) on IRD and ISD were chosen to form the final treatment recommendation model (FTR). FTR recommendations were based on the higher value between RSP and SSP. Kaplan-Meier curves were generated for patients receiving recommended (consistent group) and nonrecommended treatments (inconsistent group), which were compared using the log-rank test.
RESULTS: The study included 118 236 patients, categorized into TD (TRD: 44 621; TSD: 41 500), IVD (IRD: 4949; ISD: 4621), and EVD (22 545). Both radiotherapy and surgery models accurately predicted RSP and SSP (C-index: 0.735-0.787 and 0.769-0.797, respectively). The consistent group exhibited higher survival rates than the inconsistent group, particularly among patients not suitable for active surveillance (P < .001).
CONCLUSIONS: FTR accurately identifies patients with localized PCa who would benefit more from either radiotherapy or surgery, offering clinicians an effective AI tool to make informed choices between these 2 treatments.

OBJECTIVE: Cerebrovascular events during thrombolysis in cardiac arrest (CA) caused by pulmonary embolism (PE) is a life-threatening condition. However, the balance between cerebrovascular events and thrombolytic therapy in PE-induced CA remains a great challenge.
METHODS: In this study, we reported three unique cases regarding main concerns surrounding cerebrovascular events in thrombolytic therapy in PE-induced CA.
RESULTS: The patient in the case 1 treated with thrombolysis during CPR and finally discharged neurologically intact. The patient in the case 2 received delayed thrombolysis and died eventually. The patient in the case 3 was contraindicated to thrombolysis due to the complication of subarachioid hemorrahage and died within days.
CONCLUSIONS: Our case series highlights three proposed approaches to consider before administering thrombolysis as a treatment option in PE-induced CA patients: (1) prolonging the resuscitation, (2) administering thrombolysis promptly, and (3) ruling out cerebrovascular events.

Anterior diastema is a common esthetic defect in China. The general treatment for a patient with diastemata, including orthodontics and direct and indirect restorations, is a multidisciplinary clinical procedure covering the orthodontics, operative dentistry, general dentistry, and prosthodontics department. Given the diversity of departments and the complex etiology of this defect, decision-making regarding the closing methods and time selection is undefined and unintegrated, which makes the long-term stability of closure unpredictable. This article proposed an etiology-based decision tree with actual measurement of diastemata width for diastemata closure. The decisional steps include classifying the etiological factors based on patients\' medical history and clinical manifestation to evaluate the stability of diastemata. After maintaining the stability of diastemata, contemporary and multidisciplinary treatment plans were selected in accordance with the measured width of diastemata and patients\' cosmetic psychology, economics, and available time. These decision trees focus on the challenges of collaboration among dental departments, propose an objective and efficient ways for connections, and promote efficient and effective diastemata closure.
前牙牙间隙是一种常见的牙齿缺陷。临床常用关闭前牙牙间隙方案包括正畸治疗、直接修复以及间接修复等，是一项可能同时涉及正畸科、牙体牙髓科、修复科、全科以及牙周科等口腔门诊多科室合作的诊疗项目。由于当前我国口腔门诊普遍存在的分科过细、跨科室合作效率低，以及牙间隙的病因繁杂，使得临床跨科室合作关闭牙间隙时如何选择最佳治疗时机与合理方案尚缺乏清晰的诊疗思路，最终将影响关闭前牙牙间隙的疾病诊治效能和疗效。为此，本文推荐了一套基于病因诊断与牙间隙几何量实测值进行分类分级来关闭前牙牙间隙的三个序列临床决策树。该套决策树通过患者病史和临床检查结果进行病因归类，并判断牙间隙的稳定性，再进一步依据牙间隙几何量实测值、患者的求美心理评估、诊疗费用与时间成本等，合理选择相应的口腔门诊跨科室联合治疗方案，实现牙间隙的精准分类诊疗。本决策树针对前牙牙间隙跨科室合作难点，提供了客观高效的决策拐点，有助于进一步提高工作效率，提升关闭前牙牙间隙的疗效。.

OBJECTIVE: The blood urea nitrogen to creatinine (BUN/Cr) ratio is associated with early neurological deterioration in acute ischaemic stroke (AIS). However, the predictive value of the BUN/Cr ratio for the AIS prognosis remains unclear. Therefore, we evaluated the correlation between the BUN/Cr ratio and the 3-month outcome in patients with AIS, further testing their dose-response relationship.
METHODS: This retrospective cohort study enrolled patients with AIS who were admitted between 1 January 2013 and 31 May 2022. Poor clinical outcome was defined as 3-month Modified Rankin Scale (mRS) >2. Cox proportional HR was used to evaluate the correlation between the BUN/Cr ratio and 3-month outcome. Restricted cubic spline and robust locally weighted regression analyses were conducted to determine the dose-response relationship between the BUN/Cr ratio and the 3-month outcome.
RESULTS: A total of 4952 eligible patients were included in the study. The patients were divided into three groups according to the tertiles of BUN/Cr ratio (T1, <0.071; T2, 0.071-0.093; and T3, >0.093). After logistic regression adjustment for demographic and clinical characteristics, the BUN/Cr ratio was found to be independently associated with the 3-month outcome in patients with AIS. The restricted cubic spline and locally regression smoothing scatterplot graph showed a strong dose-response relationship between the BUN/Cr ratio and the 3-month outcome in patients with AIS.
CONCLUSIONS: A dose-response relationship was observed between the BUN/Cr ratio and the 3-month outcome in patients with AIS, suggesting that the BUN/Cr ratio could serve as a reliable predictor for the AIS prognosis.

We developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients during their stay in the intensive care unit (ICU), ultimately aiding clinical decision-making and improving patient care. Data from 2286 geriatric patients who underwent surgery and were admitted to the ICU of Dongyang People\'s Hospital between 2012 and 2021 were retrospectively analyzed. Integrated algorithms were developed, and four machine-learning algorithms were used. Selected characteristics included common demographic data, biochemical indicators, and vital signs. Eight key variables were selected using the Least Absolute Shrinkage and Selection Operator and Random Forest Algorithm. Thrombocytopenia occurred in 18.2% of postoperative geriatric patients, with a higher mortality rate. The C5.0 model showed the best performance, with an area under the receiver operating characteristic curve close to 0.85, along with unparalleled accuracy, precision, specificity, recall, and balanced accuracy scores of 0.88, 0.98, 0.89, 0.98, and 0.85, respectively. The support vector machine model excelled at predictively assessing thrombocytopenia severity, demonstrating an accuracy rate of 0.80 in the MIMIC database. Thus, our machine learning-based models have considerable potential in effectively predicting the risk and severity of postoperative thrombocytopenia in geriatric ICU patients for better clinical decision-making and patient care.

OBJECTIVE: To construct a nomogram for predicting the responsiveness of cardiac resynchronization therapy (CRT) in patients with chronic heart failure and verify its predictive efficacy.
METHODS: A retrospective study was conducted including 109 patients with chronic heart failure who successfully received CRT from January 2018 to December 2022. According to patients after six months of the CRT preoperative improving acuity in the left ventricular ejection fraction is 5% or at least improve grade 1 NYHA heart function classification, divided into responsive group and non-responsive group. Clinical data of patients were collected, and LASSO regression analysis and multivariate logistic regression analysis were used to explore relative factors. A nomogram was constructed, and the predictive performance of the nomogram was evaluated using the calibration curve and decision curve analysis (DCA).
RESULTS: Among the 109 patients, 61 were assigned to the CRT-responsive group, while 48 were assigned to the non-responsive group. LASSO regression analysis showed that left ventricular end-systolic volume, diffuse fibrosis, and left bundle branch block (LBBB) were independent factors for CRT responsiveness in patients with heart failure (P < 0.05). Based on the above three predictive factors, a nomogram was constructed. The ROC curve analysis showed that the area under the curve (AUC) was 0.865 (95% CI 0.794-0.935). The calibration curve analysis showed that the predicted probability of the nomogram is consistent with the actual occurrence rate. DCA showed that the line graph model has an excellent clinical net benefit rate.
CONCLUSIONS: The nomogram constructed based on clinical features, laboratory, and imaging examinations in this study has high discrimination and calibration in predicting CRT responsiveness in patients with chronic heart failure.

BACKGROUND: Anaemia is a severe and common complication in patients with aneurysmal subarachnoid haemorrhage (aSAH). Early intervention for at-risk patients before anaemia occurs is indicated as potentially beneficial, but no validated method synthesises patients\' complicated clinical features into an instrument. The purpose of the current study was to develop and externally validate a nomogram that predicted postacute phase anaemia after aSAH.
METHODS: We developed a novel nomogram for aSAH patients to predict postacute phase anaemia (3 days after occurrence of aSAH, prior to discharge) on the basis of demographic information, imaging, type of treatment, aneurysm features, blood tests and clinical characteristics. We designed the model from a development cohort and tested the nomogram in external and prospective validation cohorts. We included 456 aSAH patients from The First Affiliated Hospital for the development, 220 from Sanmen People\'s Hospital for external validation and a prospective validation cohort that included 13 patients from Hangzhou Red Cross Hospital. We assessed the performance of the nomogram via concordance statistics and evaluated the calibration of predicted anaemia outcome with observed anaemia occurrence.
RESULTS: Variables included in the nomogram were age, treatment method (open surgery or endovascular therapy), baseline haemoglobin level, fasting blood glucose level, systemic inflammatory response syndrome score on admission, Glasgow Coma Scale score, aneurysm size, prothrombin time and heart rate. In the validation cohort, the model for prediction of postacute phase anaemia had a c-statistic of 0.910, with satisfactory calibration (judged by eye) for the predicted and reported anaemia outcome. Among forward-looking forecasts, our predictive model achieved an 84% success rate, which showed that it has some clinical practicability.
CONCLUSIONS: The developed and validated nomogram can be used to calculate individualised anaemia risk and has the potential to serve as a practical tool for clinicians in devising improved treatment strategies for aSAH.

Owing to advances in diagnosis and treatment methods over past decades, a growing number of early-stage hepatocellular carcinoma (HCC) diagnoses has enabled a greater of proportion of patients to receive curative treatment. However, a high risk of early recurrence and poor prognosis remain major challenges in HCC therapy. Microvascular invasion (MVI) has been demonstrated to be an essential independent predictor of early recurrence after curative therapy. Currently, biopsy is not generally recommended before treatment to evaluate MVI in HCC according clinical guidelines due to sampling error and the high risk of tumor cell seeding following biopsy. Therefore, the postoperative histopathological examination is recognized as the gold standard of MVI diagnosis, but this lagging indicator greatly impedes clinicians in selecting the optimal effective treatment for prognosis. As imaging can now noninvasively and completely assess the whole tumor and host situation, it is playing an increasingly important role in the preoperative assessment of MVI. Therefore, imaging criteria for MVI diagnosis would be highly desirable for optimizing individualized therapeutic decision-making and achieving a better prognosis. In this review, we summarize the emerging image characteristics of different imaging modalities for predicting MVI. We also discuss whether advances in imaging technique have generated evidence that could be practice-changing and whether advanced imaging techniques will revolutionize therapeutic decision-making of early-stage HCC.

BACKGROUND: This systematic review aims to assist clinical decision-making in selecting appropriate preoperative prediction methods for difficult tracheal intubation by identifying and synthesizing literature on these methods in adult patients undergoing all types of surgery.
METHODS: A systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive electronic searches across multiple databases were completed on March 28, 2023. Two researchers independently screened, selected studies, and extracted data. A total of 227 articles representing 526 studies were included and evaluated for bias using the QUADAS-2 tool. Meta-Disc software computed pooled sensitivity (SEN), specificity (SPC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Heterogeneity was assessed using the Spearman correlation coefficient, Cochran\'s-Q, and I2 index, with meta-regression exploring sources of heterogeneity. Publication bias was evaluated using Deeks\' funnel plot.
RESULTS: Out of 2906 articles retrieved, 227 met the inclusion criteria, encompassing a total of 686,089 patients. The review examined 11 methods for predicting difficult tracheal intubation, categorized into physical examination, multivariate scoring system, and imaging test. The modified Mallampati test (MMT) showed a SEN of 0.39 and SPC of 0.86, while the thyromental distance (TMD) had a SEN of 0.38 and SPC of 0.83. The upper lip bite test (ULBT) presented a SEN of 0.52 and SPC of 0.84. Multivariate scoring systems like LEMON and Wilson\'s risk score demonstrated moderate sensitivity and specificity. Imaging tests, particularly ultrasound-based methods such as the distance from the skin to the epiglottis (US-DSE), exhibited higher sensitivity (0.80) and specificity (0.77). Significant heterogeneity was identified across studies, influenced by factors such as sample size and study design.
CONCLUSIONS: No single preoperative prediction method shows clear superiority for predicting difficult tracheal intubation. The evidence supports a combined approach using multiple methods tailored to specific patient demographics and clinical contexts. Future research should focus on integrating advanced technologies like artificial intelligence and deep learning to improve predictive models. Standardizing testing procedures and establishing clear cut-off values are essential for enhancing prediction reliability and accuracy. Implementing a multi-modal predictive approach may reduce unanticipated difficult intubations, improving patient safety and outcomes.