Objective To investigate the factors that influence early blockage of the catheter during continuous bladder irrigation (CBI) following transurethral resection of the prostate (TURP), and to assess associated risks.Police model construction and validation. Method A retrospective study was conducted, selecting 200 patients who underwent TURP at the First People’s Hospital of Jiujiang City between January 2022 and January 2024 as the training set.Based on whether blockage occurred early during the postoperative CBI procedure, patients were divided into a blockage group (n = 35) and a non-blockage group (n = 165). Additionally, data from January 2024 to July 2024 at this hospital were selected.
Alright. The 86 cases of TURP patients serve as the validation set. Using the factors that showed statistical significance in the univariate analysis as independent variables and whether the patient experienced tube blockage as the dependent variable, we conducted an analysis.Binary logistic regression analysis was used to identify independent influencing factors, and a risk prediction model for early occlusion of the tube (nomo graph) during the CBI process following TURP was constructed. The model was then refined.Performed a receiver operating characteristic (ROC) curve analysis. Results The univariate analysis showed that the two groups differed in preoperative prothrombin time, irrigation fluid temperature, irrigation speed, diabetes, and postoperative outcomes.There were statistically significant differences in terms of constipation, the positioning of convoluted tubes, and inadequate fixation (P < 0.05). The results of logistic regression analysis showed that preoperative prothrombin time and irrigation had an impact.Liquid temperature, flushing speed, diabetes, postoperative constipation, and inadequate fixation of the tubing’s bends are independent risk factors for early blockage during the postoperative CBI process for patients undergoing TURP.The factors and differences were statistically significant (P < 0.05). Internal validation of the nomogram model revealed an area under the receiver operating characteristic curve (AUC) of 0.967 (95% CI: 0.921–0.999).1.000), with good discrimination. The maximum Youden index is 0.563, the sensitivity is 0.711, and the specificity is 0.852. The average error between the theoretical and actual values of the calibration curve.The value is 0.027, indicating good consistency. Conclusion The constructed TURP postoperative CBI blockage risk prediction model effectively integrates preoperative prothrombin time, irrigation parameters, and other relevant factors.Independent risk factors such as diabetes exhibit strong predictive capabilities, enabling the accurate identification of high-risk patients and guiding targeted clinical interventions (e.g., optimizing flush management, controlling metabolic abnormalities).This approach effectively reduces the incidence of blockages, providing evidence-based decision-making tools for perioperative care.

Objective To investigate the differences in sleep patterns between patients with mixed features depression (MFD) and those with mixed features bipolar disorder (MFBD), and to develop predictive models. Method A cohort of 198 patients with MM/MF (n = 83) and BM/MF (n = 115) who were admitted to the hospital between January 2022 and June 2023 was selected as the modeling dataset.Use single-factor and multi-factor logistic regression to identify independent influencing factors and construct and validate predictive models. Create risk nomograms and receiver operating characteristic curves.(ROC), calibration curves, and decision curves (DCA) are used to evaluate the predictive performance of the model. A sample of cases admitted to the hospital between July 2023 and August 2024 was selected in a 7:3 ratio 85 cases of MMF and BMF patients were used as a validation set to externally validate the model. The results show that the sleep structure of MMF and BMF patients in this study exhibited characteristics of rapid eye movement (REM) sleep.There were differences in the latent period (REM), the proportion of N2-stage sleep, the proportion of N3-stage sleep, sleep efficiency (SE), and wakefulness time (P < 0.05). Multivariate logistic analysis. Results The regression analysis indicate that the body mass index (BMI), disease duration, N2 stage, N3 stage, SE, REM latency, and wakefulness duration all differ between MMf and BMf patients in terms of their sleep structure.There were significant independent factors of variability (p < 0.05). The predictive model constructed had an ROC area under the curve of 0.852 (95% CI: 0.800–0.903), with a maximum Youden index.The value is 0.558, the sensitivity is 0.739, the specificity is 0.819, and the differentiation is good; the overall trend of the model calibration curve closely approximates an ideal curve, with an average absolute error (EMA) of 0.033, the overall calibration performance is good; the net benefit level of the predictive model is high when the DCA curve threshold is between 0.40 and 1.00; Hosmer-Lemeshow test, χ.2 =5.211, P = 0.735, good fit (P > 0.05). In the external validation of this model, the ROC curve area was 0.940 (95% CI: 0.894–0.987).The maximum Youden index is 0.740, the sensitivity is 0.875, the specificity is 0.865, and the discrimination is excellent; the overall trend of the model calibration curve closely approximates an ideal curve, EMa =0.047; the overall calibration performance is good. The net benefit level of the predictive model is high when the DCA curve threshold is between 0.18 and 1.00. Høsør/Lemeshow test, χ2 =3.644, p = 0.820, good fit (p > 0.05), indicating that the model’s predictive performance is strong.Conclusion There are differences in sleep structure between patients with MMF and BMMF.IBM, disease duration, N2 stage, N3 stage, SE, REM latency, and wakefulness time are all independent factors that differentiate the sleep structure of MMF and BMMF patients.The predictive model performs well, allowing for a better understanding of patients’ sleep issues and enabling the development of more effective intervention strategies to improve patients’ quality of life.

Objective To develop a predictive model for abnormal coagulation function in patients with Acute Promyelocytic Leukemia (APL) using machine learning algorithms, and to evaluate the model’s performance.Evaluate the effectiveness. Method Select 140 patients with APL who were admitted to a hospital between January 2020 and July 2023 as the validation group. Determine based on whether the patients experienced complications.The coagulation function abnormalities were divided into a group that experienced them (n = 96) and a group that did not experience them (n = 44). Additionally, 60 patients with APL from August 2023 to January 2025 were selected as a validation group.
Collect laboratory indicators and clinical data from patients, perform univariate analysis, and employ logistic regression, decision-classification regression tree (DCRT), and backpropagation neural networks.(BPNN) developed a machine learning algorithm to create a predictive model for the occurrence of abnormal coagulation function in patients with APL, using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).Compare the predictive value of models constructed using three different methods. Results The results of the univariate analysis indicate that there are differences in bleeding, risk stratification, and activated partial thromboplastin time between the two patient groups.(APTT), prothrombin time (PT), lactate dehydrogenase (LDH), white blood cell count (WBC), tumor necrosis factor.α (TNF)- α), Interleukins -6 (I L)-6), D-Dimer (D) There were statistically significant differences in the levels of fibrinogen degradation products (FDP), fibrinogen (FIB), and other markers (P < 0.05). The three machines differed.
The areas under the receiver operating characteristic curve (AUC) for the models constructed using learning algorithms were all greater than 0.800, indicating good predictive accuracy (P < 0.05). Specifically, the BPNN model predicted that patients with APL would experience thrombosis.The performance of the blood function anomaly model was optimal, with an AUUC of 0.950 (95% CI: 0.916 to 0.984). The model demonstrated good discriminative ability. The DCA curve table based on the BPNN model.It is evident that the model performs significantly better than the “full intervention” and “no intervention” strategies when the probability threshold for high risk is set at 0.10. This suggests that the threshold probability of ≥ 0.10 can be used.Identify the optimal risk threshold for initiating interventions to address abnormal coagulation function in patients with APL. Conclusion The occurrence of abnormal coagulation function in APL patients is associated with the aforementioned 11 characteristic factors.There is a strong correlation. The predictive models for APL patients’ occurrence of abnormal coagulation functions, which are built using machine learning algorithms, all exhibit good predictive performance. Among them,The BPNN model exhibits the highest predictive performance.

Objective To explore the influencing factors of postoperative anxiety and depression in patients with gallstone disease using structural equation modeling, and to conduct a path analysis to inform the development of treatment strategies.
The study provides theoretical insights into effective intervention measures. Method A convenience sampling method was employed to select 200 cases admitted to the general surgery department of a hospital between February 2023 and May 2024.Patients with gallstone disease who underwent surgical treatment were selected as the research subjects, and general data questionnaires, the Self-Perceived Burden Scale (SPBS), the Anxiety Self-Assessment Scale (ASAS), and the Depression Self-Assessment Scale (DSAS) were used.Self-assessment scales (SDs) and quality of life scales (GIQLI) were used to conduct surveys, and a multi-mediator structural equation model was employed to analyze postoperative anxiety and depressive states in patients with gallstone disease influencing factors. The total scores for the Self-Perceived Burden Scale for patients with gallstone disease were (28.25 ± 4.82) points, and the total scores for the Anxiety Self-Rating Scale were (52.61 ±) points. At 6.10, the total scores for the depression self-assessment scale were (49.67 ± 5.67) points, and the total scores for the quality of life scale were (103.38 ± 7.68) points. Results The Pearson correlation analysis.The results show that the self-perceived burden scale for patients with gallstone disease exhibits a significant positive correlation with the anxiety self-assessment scale and the depression self-assessment scale, and a significant negative correlation with the quality of life scale.Significant difference (p < 0.05). The Self-Perceived Burden Scale for patients with gallstone disease was positively correlated with physical burden, emotional burden, and economic burden, as well as with perceived symptoms and physiological function.There was a negative correlation between cognitive status, psychological status, and social activity status (p < 0.05). The results of the mediation effect analysis showed that performing 2,000 correct bookstrokes had an impact.The examination revealed that the direct effect of patients with gallstone disease on their subjective burden on quality of life was –1.790 (95% CI: –1.462 to –1.981), and the indirect effect was–0.615, (95% CI: –0.650 to –0.522). Anxiety and depressive states exhibit partial mediation between self-perceived burden and quality of life. Conclusion Post-operative anxiety and depression are significant factors that affect patients’ sense of burden and quality of life. Clinicians should be vigilant about these issues in their work.To reduce anxiety, depression, and negative emotions among individuals with gallstone disease, thereby alleviating life stress and improving overall quality of life.

Objective To analyze the influence factors of adverse reactions after treatment of schizophrenia in women with non-convulsive electroshock (MECT) at different wavewidths, construct predictive models and explore intervention responses. Methods To select 323 female patients with schizophrenia who received MECT treatment in the psychiatric department of Shaoxing 7 People's Hospital from June 2022 to May 2024. Based on whether or not an adverse reaction occurred after treatment, the group was divided into an adverse response group (49 cases) and a non-negative response group (274 cases). The patient's general information, disease-related indicators, etc., are performed independent sample t tests and card tests, and statistically significant factors are included in multifactorial logistic regression analysis to determine independent influence factors for the occurrence of adverse reactions. Linear predictive models are constructed based on the results of multi-factor analysis, and the accuracy and differentiation of models are assessed by calibration curves and subjects' working characteristics (ROC) curves.Results The multifactorial logistic regression analysis showed that the therapeutic wavelength (OR = 0.041, 95% CI: 0.014 to 0.120) and the combination drug type (OR / 0.328,95% CI: 0.213 ~ 0.503), PANSS score (OR = 0.936, 95% CI 0.907 ~ 0.967), Oxygen absorption (OR / 1.111, 95%CI 0.053 ~ 0.234),Psychological intervention (OR = 4.487, 95% CI: 2.370-8.494) and occupation (OR / 2.403, 95%CI: 1.201-4.87) were independent influences on the occurrence of adverse reactions (P < 0.05).The line chart predicts model calibration curves predicting probabilities are well aligned with actual probabilities,
The ROC curve area is 0.937 (95% CI: 0.908 to 0.967), and the model has excellent differentiation and calibration. Conclusions The influence factors of adverse reactions after MECT treatment for schizophrenia in women at different wavewidths were identified, and the predictive model constructed can inform clinical prevention. Targeted interventions can help reduce the risk of adverse reactions and improve the safety and effectiveness of treatment.

Objective To explore the influencing factors of multi-drug resistant organism (MDRO) infections in patients with diabetic foot ulcers (DFUs) in older adults, and to construct a risk prediction model based on independent influencing factors. Method A retrospective study was conducted on 200 elderly DFU patients who were hospitalized at a hospital between June 2022 and June 2023. The patients were categorized based on their characteristics.
Were there any concurrent infections during the hospital stay? The MDROs infection was divided into an infected group (n = 66) and a non-infected group (n = 134). Clinical data of the patients were collected using the hospital’s electronic medical record system. Independent influencing factors were identified through univariate analysis and multivariate logistic regression analysis, and a predictive model was constructed based on these findings. R4.2.1 was used to create corresponding nomograms, and the model’s predictive performance was evaluated using patient work characteristics (ROC) curves and calibration curves. Additionally, 86 DFU patients admitted between July 2023 and March 2024 were selected as a validation set. The ROC and calibration curves were plotted using data from this validation set to externally validate the model’s predictive performance. Results Univariate and multivariate logistical regression analysis showed that age, ulcer area, Wagnér grade, neuroischemic wound, hospital stay duration, duration of antimicrobial therapy use, pre-hospital antimicrobial treatment history, invasive procedures, fasting blood glucose, and hypersensitive C-reactive protein (hs-CRP) were significant predictors. CRP) and associated osteomyelitis were independent factors influencing the co-occurrence of MDROs infection (p < 0.05). The area under the curve (AUC) of the risk prediction model constructed based on these 11 independent factors was 0.912, with the optimal cutoff value.
The corresponding sensitivity and specificity were 0.848 and 0.881, respectively. The model demonstrates good discriminative ability. The calibration curve results show an average absolute error of 0.030, indicating that the calibration curve closely follows the ideal curve, suggesting that the model’s calibration performance is excellent. The validation set ROC curve and calibration curve results are favorable, indicating that the model has good external predictive performance. Conclusion The presence of MDROs infection in elderly DFU patients can be predicted based on factors such as age, ulcer indicators, and antibiotic usage patterns. A risk prediction model constructed using 11 independent factors demonstrates excellent predictive performance. Developing prevention strategies based on this model can help prevent or reduce the occurrence of MDROs infections.

Objective To analyze the main factors influencing the hospitalization costs of patients with angina pectoris under different types of medical insurance, providing a reference basis for rational control of hospitalization costs. Methods We collected the hospitalization records of 11,259 patients with angina pectoris from a certain city’s hospital between January 1, 2021, and June 30, 2022, categorized them into different types of medical insurance, and analyzed the influencing factors of hospitalization costs using multiple linear regression analysis. Results The hospitalization costs per capita differed among patients with different types of medical insurance. The average hospitalization cost per capita was highest for urban employees with basic medical insurance and lowest for those who paid entirely out-of-pocket. This difference was statistically significant (P < 0.05). Multiple linear regression analysis indicated that gender, marital status, method of medical payment, mode of admission, whether or not to undergo surgery, and length of stay were influencing factors of hospitalization costs (P < 0.05).  Based on standardized regression coefficients, the main influencing factors were surgery, length of stay, and mode of admission. Conclusion Different types of medical insurance have some impact on hospitalization costs. Patients who undergo surgery, have longer lengths of stay, and are admitted through emergency services tend to incur significantly higher costs.

Objective To construct a system of indicators for job competency evaluation for standardized training of graduate interns with specialized degrees in clinical medicine and to determine the weight of indicators at each level.Methods Adopting an improved Delphi law organization of two rounds of expert consultations (round 1 n = 35, Round 2 n = 21, assessing the importance and feasibility of the indicators based on the Likert scale at level 5, calculating mean and coefficient of variance (CV), and examining the coordination of expert opinions with Kendall's W; Calculate the authority factor (Cr) based on expert familiarity (Cs) and the basis of judgment (Ca). On this basis, layer analysis (AHP) is applied to build a judgment matrix and empower it, while a consistency test (CI, CR) is performed. The confidence was assessed by Cronbach's alpha and the structural effectiveness was evaluated by KMO and Bartlett spherical tests. An exploratory factor analysis (EFA) was conducted. Results The evaluation system for seven levels, 14 levels and 38 levels of indicators was established.Two rounds of the Kendall ′ s W Level 3 indicator increased from 0.621 to 0.857, respectively;0.632 rose to 0.765;0.545 rose to 0.810 (P < 0.01), and the expert authority coefficients Cr were 0.875 and 0.885. Coherence within the scale Cronbach ′ s α = 0.9902; KMO = 0.871, the Bartlett test P < 0.001, and the EFA's common factor load is > 0.50. AHP consistency ratio CR = 0.0142 < 0.10, passed the consistency test. Conclusion The resulting system of job competency indicators, covering the seven dimensions of knowledge, clinical, learning, scientific innovation, teamwork, communication and professional literacy, was well weighted and could be used for process and final evaluation of graduate standardized training and as a basis for quality improvement.

Objective To explore the dual mechanisms by which sedentary behavior affects the risk of pre-diabetes, through both direct metabolic pathways and indirect pathways mediated by waist circumference, based on data from the National Health and Nutrition Examination Survey (NHANES). Method Using multivariate logistic regression, restricted cubic splines, segmented regression, and mediation analysis, we evaluated the association between sedentary time and pre-diabetes based on NHANES data from 2011 to 2020. Results A total of 15,243 participants (6,696 with pre-diabetes; 8,547 with normal glucose metabolism) were included. The waist circumference of the normal glucose metabolism group was significantly lower than that of the pre-diabetes group (P < 0.01). The average HbA1c was (5.40 ± 0.37)%. Sedentary time showed a non-linear relationship with the risk of pre-diabetes (P = 0.021), with a threshold value of 490 minutes/day. In the low sedentary group (<490 minutes/day), each additional minute of sedentary time per day resulted in a 0.0022 unit increase in HbA1c, with significant direct effects as well; in the high sedentary group (≥490 minutes/day), only the waist circumference mediated the effect on HbA1c, with a higher effect value. Conclusion There is a threshold effect of 490 minutes/ day for sedentary time and its relationship with pre-diabetes. When sedentary time is below 490 minutes/day, there is both partial mediation by waist circumference and direct effects; after surpassing this threshold, there is only complete mediation by waist circumference.

Objective To analyze the health literacy level of residents in Binzhou City in 2024, explore the differences in health literacy among different populations and their association with major public health problems, so as to provide scientific evidence for formulating effective health education and promotion strategies in the future. Methods Stratified cluster multistage random sampling was adopted. Household surveys were conducted among permanent residents aged 15–69 years using the National Resident Health Literacy Monitoring Questionnaire. SPSS23.0 statistical software was used for data analysis. The chi-square test was applied to compare the overall health literacy level and differences across demographic characteristics, with the test level set at α=0.05. Results The overall health literacy level among urban and rural residents aged 15–69 years in Binzhou City in 2024 was 38.03% (95%CI:36.85%–39.21%, calculated based on valid sample size n=6511). In terms of three dimensions of health literacy, the level of healthy lifestyle and behavior literacy (40.21%) > basic knowledge and concept literacy (36.26%) > basic skill literacy (29.70%). For six categories of health issues, safety and first-aid literacy (56.57%) > scientific health concept literacy (43.54%) > basic medical literacy (42.96%) > infectious disease prevention and control literacy (39.52%) > chronic disease prevention and control literacy (25.05%) > health information literacy (23.85%). The health literacy level of rural residents was lower than that of urban residents (χ²=12.43, P＜0.001). Female residents had a lower health literacy level than male residents (χ²=11.81, P＜0.001). The 15–30 years age group achieved the highest health literacy level (59.17%), while the 61–69 years group had the lowest level (27.28%) (χ²=237.85, P＜0.001), indicating that health literacy decreased significantly with advancing age (for population over 15 years old) (r=-0.988, P＜0.05). Residents with primary school education or below had the lowest health literacy level (22.91%), and those with college education or above had the highest level (74.12%) (χ²=432.40, P＜0.001), which suggested that health literacy increased with higher educational attainment (r=0.998, P＜0.05). Staff of government agencies and public institutions showed the highest health literacy level (65.54%), whereas farmers and other occupational groups had the lowest level (34.24%) (χ²=185.10, P＜0.001). Conclusion The overall health literacy level of residents in Binzhou City maintains an upward trend, but disparities exist among populations with different demographic characteristics. It suggests that while continuously carrying out resident health literacy monitoring in the future, targeted and effective health education interventions should be implemented for key populations.

Objective To explore the association between childhood left-behind experience and mental health among college students. Methods A mixed research method combining quantitative survey and qualitative interview was adopted to carry out an empirical study in 3 universities of different types in a city of Shandong Province. In the quantitative survey, multistage cluster sampling was used to recruit 1605 college students (including 312 left-behind college students). Qualitative interviewees consisted of 40 left-behind college students and 1 psychological counselor. Results (1) The positive rate of mental health problems among left-behind college students (35.58%) was significantly higher than that of students without left-behind experience (19.95%). For the 312 left-behind college students, the positive rates of mental health problems across 10 dimensions ranked as follows: obsessive-compulsive symptoms > interpersonal sensitivity > hostility > depression > paranoia > others > phobic anxiety > anxiety > psychoticism > somatization. Multivariate logistic regression analysis showed that after adjusting for other covariates, the duration of parents' migrant work was a protective factor against phobic anxiety. Participants whose parents had been migrant workers for more than 3 years had a 63% lower risk of phobic anxiety compared with those whose parents worked away for 1 year (OR=0.37, 95%CI:0.16–0.83). In terms of living arrangement, students living alone had 5.65 times higher risk of somatization (OR=5.65, 95%CI:1.86–17.13), 3.05 times higher risk of obsessive-compulsive symptoms (OR=3.05, 95%CI:1.33–7.00), and 4.54 times higher risk of phobic anxiety (OR=4.54, 95%CI:1.67–12.33) than those living with one parent. Regarding campus interpersonal relationships, students with "average" relationships with teachers and classmates exhibited higher risks of somatization (OR=2.64, 95%CI:1.40–5.00), anxiety (OR=2.50, 95%CI:1.37–4.53) and phobic anxiety (OR=2.51, 95%CI:1.40–4.49), which were 2.64, 2.50 and 2.51 times those of students with "good" relationships, respectively. (2) Left-behind college students mainly suffered from anxiety, depression, obsessive-compulsive symptoms and interpersonal sensitivity. Psychological counseling experts also indicated that anxiety and obsessive-compulsive symptoms were the most prevalent psychological problems among left-behind college students. Conclusion Childhood left-behind experience is an important influencing factor for college students' mental health. It suggests that mental health education for college students should be implemented from a life-course perspective.

Objective To explore the application effect of risk intervention based on binary logistic regression analysis in the preventive management of defective loaner instrument packages in the Central Sterile Supply Department (CSSD). Methods A total of 298 loaner instruments registered from April 2023 to December 2023 were retrospectively selected as the modeling group through the CSSD information traceability system of a hospital. According to the defect status of loaner instruments, they were divided into the defect group (n=52) and the qualified group (n=246). Univariate analysis and binary logistic regression were used to identify independent risk factors, construct a risk prediction model, and analyze the application of risk intervention in the preventive management of defective loaner instrument packages in CSSD. Another 128 loaner instruments registered in the hospital from January 2024 to April 2024 were selected as the verification group for external verification of the predictive performance of the model. Furthermore, 141 loaner instruments registered from May 2024 to August 2024 were managed with risk intervention based on the risk prediction model. Results The incidence of defective loaner instrument packages in CSSD was 17.45% (52/298). Univariate analysis showed that unused contaminants on instruments with service life limits, compliance of supplier qualification and training, standardized loading, standardized pretreatment, standardized operation of cleaning equipment, correct concentration of cleaning agent, standardized cleaning procedures, qualified disinfection and sterilization, post duty performance, and sound systems/procedures/plans were influencing factors for defective loaner instrument packages in CSSD (P＜0.05). Binary logistic regression analysis indicated that using contaminated instruments with service life limits, non-compliance of supplier qualification and training, non-standard loading, non-standard pretreatment, non-standard operation of cleaning equipment, incorrect concentration of cleaning agent, non-standard cleaning procedures, failure of disinfection and sterilization, failure to perform post duties, and imperfect systems/procedures/plans were independent risk factors for defective loaner instrument packages in CSSD (P＜0.05). The area under the receiver operating characteristic (ROC) curve of the risk prediction model was 0.866 (95%CI:0.810–0.923), the maximum Youden index was 0.605, the sensitivity was 0.731, and the specificity was 0.874, indicating good discrimination. The Hosmer-Lemeshow test for the nomogram fitting effect yielded χ²=8.082, P=0.325, showing good fitting degree (P＞0.05). The overall trend of the model calibration curve was close to the ideal curve, with favorable calibration performance (P＜0.05). When the threshold probability of the decision curve analysis (DCA) for defective loaner instrument packages in CSSD ranged from 0.07 to 0.83, the net benefit of applying the prediction model was relatively high. The external verification confirmed favorable predictive efficiency of the model. After risk intervention targeting the above independent risk factors of defective loaner instrument packages in CSSD identified by binary logistic regression analysis, the theoretical and operational assessment scores of CSSD staff were higher than those before intervention, and the incidence rate of defective packages (2.13%) was lower than before intervention, with statistically significant differences (P＜0.05). Conclusion The risk prediction model constructed based on independent risk factors for defective loaner instrument packages in CSSD has good predictive performance. The application of risk intervention based on binary logistic regression analysis to the preventive management of loaner instrument packages in CSSD can improve staff awareness of preventive management and reduce the risk of defective packages.

Against the backdrop of smart hospital construction, medical data governance serves as a crucial underpinning for improving hospital management efficiency and advancing digital transformation. To address the challenges existing in data governance of public hospitals, including multi-source heterogeneous data, decentralized storage, insufficient standardization, inconsistent statistical calibers and low data utilization efficiency, a full-process governance system covering organizational, institutional and technical dimensions is established. A special leading group for data governance has been set up, relevant data management regulations and communication mechanisms improved. Combined with a multimodal big data platform, data cleansing and post-structural processing, construction of standardized coding systems as well as deployment of data security technologies are implemented. The system effectively eliminates information silos, improves data quality and sharing efficiency, and delivers high-quality data support for clinical decision-making, operational optimization and scientific research innovation.

Objective To explore the influencing factors of ambiguous medical cases in hospitals, so as to provide evidence for reducing such cases. Methods A retrospective analysis was conducted on 136,259 discharged medical insurance settlement cases from a tertiary hospital in 2023. The CHS-DRG 2.0 information platform was adopted to screen ambiguous cases. Chi-square test and multivariate logistic regression were used to analyze the influencing factors for the occurrence of ambiguous cases. Results Ambiguous cases accounted for 3.08% (4195 cases). Univariate analysis showed that ambiguous cases were correlated with age, gender, length of hospital stay, discharge department, inter-department transfer, patient outcome (dead or alive), tumor diagnosis, number of secondary diagnoses and number of surgical procedures (P＜0.05). Multivariate analysis indicated that age, gender, discharge department, inter-department transfer, death outcome, length of hospital stay, number of secondary diagnoses and number of surgical procedures were risk factors for ambiguous cases (P＜0.05), while tumor diagnosis was a protective factor (P＜0.05). Conclusion Age, gender, discharge department, inter-department transfer, death outcome, length of hospital stay, number of secondary diagnoses and number of surgical procedures are closely associated with the generation of ambiguous cases. Hospitals should strengthen standardized training on the completion of the front page of medical records for clinicians and coders to reduce ambiguous cases.

Objective The assumption of randomness for missing data in clinical studies is difficult to verify. Sensitivity analysis can be adopted to test the reliability and robustness of results obtained from missing data handling. Methods A practical example was used to analyze the effect of albumin administration on C-reactive protein levels in patients with sepsis. Complete case analysis and multiple imputation were performed to handle binary outcome variables with missing values, and threshold analysis was applied for sensitivity analysis of missing data. Results The results of threshold analysis indicated that a critical point emerged when 3 cases in the control group were converted to positive outcomes. The conclusion was consistent with those from complete case analysis (P=0.78) and multiple imputation (P=0.89), which verified the robustness and reliability of the analytical results. Conclusion Threshold analysis serves as an available sensitivity analysis method for missing data. It can verify the robustness and reliability of findings and provide visualized evidence for judgment. This study offers a new strategy for clinical researchers dealing with missing binary outcome data. The SAS code can be accessed by scanning the OSID QR code.