Objective To analyze the risk factors and protective factors for the reversal from mild cognitive impairment to normal cognition in patients with early Alzheimer's disease, and to construct a nomogram prediction model accordingly. Methods A total of 248 patients with early Alzheimer's disease admitted to Ganzhou Third People's Hospital from January 2018 to January 2020 were prospectively enrolled and followed up for 24 months. Baseline data and clinical diagnostic data were recorded. Patients who progressed from normal cognition to mild cognitive impairment or from mild cognitive impairment to Alzheimer's disease were divided into the non-reversal group; patients with reversal from mild cognitive impairment to normal cognition were divided into the reversal group. Univariate analysis and multivariate Logistic regression analysis were adopted to screen the risk factors and protective factors affecting cognitive function reversal. The nomogram model was constructed based on independent influencing factors. The area under ROC curve (AUC) was used to evaluate the predictive efficiency, calibration curve to assess calibration degree, and DCA decision curve to evaluate clinical benefit. ROC curve and calibration curve were used for internal validation. Meanwhile, 106 patients with early Alzheimer's disease admitted to the hospital from February 2020 to February 2021 were selected as the validation set for external validation. Results Among the 248 included patients, 182 cases were in the non-reversal group (39 cases progressed from normal cognition to mild cognitive impairment, 143 cases progressed from mild cognitive impairment to Alzheimer's disease), and 66 cases were in the reversal group, with a cognitive function reversal rate of 26.61%. Multivariate Logistic regression showed that marital status (single), BMI grade (obesity), APOE4 gene carriage, sleep disorders and low Tau protein level were independent risk factors for cognitive function reversal in patients with early Alzheimer's disease (P<0.05). Education level (junior high school, senior high school and above), cognitive intervention and high MMSE score were protective factors (P<0.05). The AUC of the model was 0.948 (95%CI:0.922~0.973), with sensitivity of 97.0%, specificity of 80.8%, maximum Youden index of 0.778 and optimal cut-off value of 0.166, which presented good discrimination. Internal validation showed that the trend of actual values was basically consistent with predicted values in the calibration curve, with good accuracy. The decision curve indicated that the model could provide significant clinical net benefit. External validation with the validation set proved that the corresponding calibration curve was close to the standard curve, suggesting favorable calibration consistency. Conclusion The established nomogram model can effectively identify the risk factors of cognitive function reversal in patients with early Alzheimer's disease. It can help clinicians formulate targeted intervention measures, promote the reversal of cognitive function, and reduce the risk of disease progression.

Objective To explore the effect of palliative care under the medical community model on quality of life and negative emotions in patients with advanced liver cancer.Methods A total of 94 patients with advanced liver cancer admitted to a hospital from August 2022 to November 2023 were selected and divided into the routine group (n=47, routine nursing) and the medical community group (n=47, palliative care under the medical community model) by the random number table method. The emotional status [assessed by Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS)], cancer-related fatigue (CRF) [assessed by Cancer Fatigue Scale (CFS)] and quality of life [assessed by Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep)] were compared between the two groups on the day of discharge and 3 months after discharge.Results At 3 months after discharge, the SAS, SDS and CFS scores of the two groups were lower than those on the day of discharge (P<0.05), and the above scores in the medical community group were lower than those in the routine group (P<0.05). The 5 dimension scores of FACT-Hep in both groups were higher than those on the day of discharge (P<0.05), and were higher in the medical community group (P<0.05).Conclusion The application of palliative care under the medical community model for patients with advanced liver cancer can significantly relieve negative emotions, and effectively improve cancer-related fatigue and quality of life.

Objective To construct a structural equation model and analyze the influence path of various factors on the recovery level of patients with schizophrenia. Methods Using the convenience sampling method, patients with schizophrenia treated in a hospital in Shaoxing from February 2022 to February 2024 were selected as the research objects. A general information questionnaire, the Recovery Assessment Scale (RAS), the Perceived Social Support Scale (PSSS), the Link Stigma Scale, and the General Self-Efficacy Scale (GSES) were used for the survey. Pearson correlation analysis was used to analyze the correlation between each influencing factor and the recovery level of patients with schizophrenia. SPSS Amos 28.0 software was applied to construct a structural equation model for path analysis of each influencing factor. Results The scores of RAS, PSSS, GSES and Link Stigma Scale of 275 patients with schizophrenia were (87.52±12.46), (61.26±5.54), (23.25±4.38) and (127.43±8.24), respectively. Pearson correlation analysis showed that the RAS score of patients with schizophrenia was positively correlated with the PSSS score and GSES score (r=0.810, 0.811, P<0.05), and negatively correlated with the Link Stigma Scale score (r=-0.771, P<0.05). Structural equation model analysis showed that social support had a negative effect on stigma (path coefficient=-0.973, P<0.001), and positive effects on self-efficacy and recovery level (path coefficients=0.656, 0.649, P<0.05); stigma had a negative effect on recovery level (-0.243, P<0.05), and self-efficacy had a positive effect on recovery level (0.117, P<0.05); stigma had a negative effect on self-efficacy (-0.292, P<0.05). Conclusion The recovery level of patients with schizophrenia is affected by multi-path interactions. Social support can directly affect the recovery level, and also indirectly affect it through self-efficacy and stigma, which play partial mediating roles in this process.

Objective To analyze the risk of pulmonary thromboembolism (PTE) after radical resection of lung cancer based on a nomogram prediction model. Methods A total of 180 patients who underwent radical resection of lung cancer in the Fifth People's Hospital of Ganzhou from June 2023 to January 2024 were retrospectively selected as the modeling cohort. According to the presence or absence of postoperative PTE, they were divided into non-PTE group (n=142) and PTE group (n=38). Another 54 patients with radical resection of lung cancer from February 2024 to June 2024 were enrolled as the validation cohort. The baseline data of patients were collected, and clinical data were compared between the two groups. Multivariate logistic regression was used to analyze the risk factors of PTE after radical resection of lung cancer, and a nomogram risk prediction model for pulmonary thromboembolism after radical resection of lung cancer was constructed. Results Univariate analysis showed that pathological type, TNM clinical stage, smoking and serum D-dimer (D-D) were influencing factors of postoperative PTE (P<0.05). Multivariate logistic regression indicated that adenocarcinoma, TNM clinical stage 2–3, smoking and serum D-D≥500 mmol/L were independent risk factors for postoperative PTE (P<0.05). The area under the ROC curve was AUC=0.840 (95%CI:0.869–0.911), with good discrimination, the sensitivity was 0.711 and the specificity was 0.852. The theoretical value and actual value of the calibration curve were 0.037, with good consistency. Conclusion Pathological type of adenocarcinoma, TNM clinical stage 2–3, smoking and serum D-D≥500 mmol/L are independent risk factors for pulmonary thromboembolism after radical resection of lung cancer. The prediction model constructed based on the above factors has high predictive value.

Objective To investigate the risk factors associated with preoperative joint stiffness in patients with massive, irreparable rotator cuff tears who undergo joint capsule reconstruction surgery, and to develop a predictive model. Method A total of 83 patients with massive, irreversible rotator cuff tears who underwent joint capsule reconstruction surgery at a particular hospital between January 2020 and June 2024 were selected as the study subjects. The patients were divided into an “stiffness group” (n = 38) and a “non-stiffness group” ( n = 45) based on whether they exhibited preoperative joint stiffness. Clinical data were collected, and a single-factor analysis was performed.
Analysis: Performed ROC curve analysis on continuous variables with statistically significant differences in univariate analysis; Applied logistic regression analysis to identify independent risk factors for preoperative joint stiffness in patients with massive, irreparable rotator cuff tears who underwent repair of the joint capsule; Used software R 4.2.1 to construct a nomogram prediction model to correct the curves and decision curves for evaluating the model’s predictive performance. Results The stiffness group had higher age and disease duration compared to the non-stiffness group, and lower preoperative American Shoulder and Elbow Surgeons (ASES) scores compared to the non-stiffer group; The proportion of individuals with a history of smoking was higher in the stiffness group than in the non-stiffness group; the proportion of individuals with a history of standardized preoperative physical therapy and fatty infiltration grades 1–2 was lower in the stiffness group than the non-stiffer group, all differences were statistically significant (P < 0.05). The logistic regression analysis showed that age, disease duration, smoking history, standardized preoperative physical therapy history, fatty infiltration grade, and preoperative ASES were independent risk factors for preoperative joint rigidity in patients undergoing repair of the joint capsule for massive, irreparable rotator cuff tear (P < 0.05); A nomogram prediction model was constructed based on these factors.C The index is 0.847 (95% CI: 0.803–0.890). The risk threshold is > 0.08. Conclusion Based on age, disease duration, smoking history, preoperative standard physiotherapy history, fatty infiltration.A hierarchical, preoperative ASE score-based column chart prediction model was developed to assess the predictive value of preoperative joint stiffness in patients with massive, irreparable rotator cuff tears treated with capsule reconstruction surgery.This is quite useful for optimizing surgical procedures and enhancing treatment outcomes.

Objective To explore the role of the ARIMA model in predicting patients with schizophrenia at hospital outpatient clinics. Method Based on data from January 2011 to 2022. Analyze historical data on the number of initial diagnoses of schizophrenia in December, establish an ARIMA model, and make predictions. The modeling process includes sequence stability testing and stabilization procedures.Model identification, parameter estimation, model validation, and forecasting. This study utilized the ARIMA module within SPSS 26.0 software to analyze data from this hospital from January 2011 to 2022.A predictive model was developed based on data from initial visits for schizophrenia in December, and the predictive performance of the model was evaluated. Results ACF and PACF of the residuals in the ARIMA model.
All values fall within the 95% confidence interval. The prediction line of the ARIMA model closely aligns with the actual line, with a good degree of correspondence. The only exception is January, where the relative error was 21.15%.
Furthermore, the relative errors for the remaining 5 months all remained within 18%. Additionally, the actual number of cases for all months fell within the 95% confidence interval of the predicted values. Conclusion This analysis was conducted for The ARIMA model for predicting hospital cases of schizophrenia performed well, demonstrating strong predictive capabilities for the number of initial diagnoses of schizophrenia at hospital outpatient clinics.

Objective To understand the composition and influencing factors of hospitalization costs for diabetes patients, providing a reference for reducing the burden of diabetes on patients. Method Retrospective analysis.Collect information on diabetic patients discharged from a top-tier hospital in Zhejiang Province between January 2018 and December 2023. The composition of hospital expenses is analyzed using descriptive statistics, employing multiple methods.Linear regression and path analysis were used to examine the direct and indirect factors influencing hospital costs. Results: A total of 4,020 diabetic patients were included, with an average hospital cost.
RMB 11,967.47, average hospital stay of 13.01 days. The proportion of diagnostic costs in diabetes-related hospital expenses increased slightly from 2018 to 2023, while the trend of declining pharmaceutical costs was evident.
The top 5 factors influencing the total cost of hospitalization for diabetic patients are: the number of days spent in the hospital (0.696), the number of discharge diagnoses (0.283), and age.(0.215), DRGs reform (−0.213), surgical treatment (0.120). Length of hospital stay, surgical treatment, severity of illness (critical), mode of admission (emergency), first-time admission.Admission to the hospital can directly impact the cost of hospitalization. Other factors influence hospitalization costs indirectly through the number of days spent in the hospital. Conclusion: The number of days in the hospital, the number of discharge diagnoses, and age are factors that influence hospitalization costs.
An important factor in the cost of hospitalization for diabetic patients. While hospitalization costs for diabetes have been well-controlled and are showing a downward trend, it is still essential to minimize hospital stays as much as possible in clinical practice.Standardize treatment and care procedures, optimize the internal structure of inpatient costs, and thereby reduce the financial burden of diabetes on patients.

Objective To investigate the impact of DRGs-based payment implementation on the hospitalization costs for patients undergoing surgery for uterine fibroids, as well as changes in the cost structure, with a view to informing the implementation of DRGs-based payment.This study provides a reference framework for reducing hospital costs and improving hospital management for patients undergoing surgery for uterine fibroids. Method: The selected period for discharge was from January 1 to December 31, 2021.The patients who underwent surgery for uterine fibroids belonged to the DRGs pre-payment group, while those who were discharged between January 1 and December 31, 2023, belonged to the DRGs post-payment group. The analysis employed t-tests and χ2 tests.The data on age, length of hospital stay, and hospital expenses for a total of 2,177 patients undergoing uterine fibroid surgery in two groups were compared and analyzed using structural changes.We analyzed the composition and trends of changes in the costs associated with hospitalization for both groups. Results Show that before and after the implementation of DRGs, the average length of hospital stay, total costs, examination and laboratory fees, treatment expenses, medication costs, and other expenses remained largely unchanged.The differences in material costs were statistically significant (P < 0.05). The total costs, examination and laboratory fees, medication expenses, and material costs all decreased. The total costs had dropped from 18,033.44 before the implementation of the policy.(15,969.10, 20,186.78) yuan decreased to 15,904.71 (14,171.53, 17,966.67) yuan following the implementation of the policy. The most significant decrease was observed in the cost of medications.The proportion of fees and examination/laboratory costs has decreased, with corresponding structural change values of –8.97% and –1.63%, respectively. The proportion of treatment-related costs has increased, with a structural change value of 9.69%. Conclusion The implementation of DRGs has effectively reduced the hospitalization costs for patients undergoing surgery for uterine fibroids. There has been a noticeable improvement in the structure of hospitalization costs, with a positive shift in the proportion of treatment-related costs and reductions in medication and examination expenses.The negative change in the composition of laboratory fee structures has enhanced the value of medical staff’s technical skills. This information can serve as a reference for further optimizing hospital management and improving the control of DRG costs.

Objective To investigate the grouping effects and factors influencing hospital costs among patients undergoing breast cancer surgery under the DGR grouping system. Method Analysis conducted on January 1, 2021.As of December 31, 2023, the distribution of 1,812 breast cancer surgery patients at a tertiary public hospital in Jiangmen City according to the DRG system, using the coefficient of variation and the overall variance reduction method.Evaluate the effectiveness of the grouping method. Analyze the changes in the structure of hospitalization costs for different DRG groups using structural variation values and structural variation degrees. Employ logistic regression analysis to examine hospitalization patterns.Factors Affecting Costs. Results Patients undergoing breast cancer surgery showed better outcomes in the DRG group. Hospital expenses accounted for a higher proportion, including diagnostic fees, treatment costs, consumables, and medications.Fee. JA13Total mastectomy for malignant breast tumors, with complications and associated conditions group vs. JA15.The comprehensive medical service costs for the group undergoing a total mastectomy for malignant breast tumors, compared to the average length of stay.The correlation with hospital costs was the strongest. The results of the univariate analysis showed that comorbidities, payment methods, age, and length of hospital stay were factors that influenced hospital costs. The results of the t-test regression analysis indicate that JA13. The factors influencing hospital costs for the group undergoing a total mastectomy for malignant breast tumors were hospital stay duration ≥ 15 days and age 55–64 years; JA15. The factors influencing the costs of hospitalization for patients undergoing a total mastectomy for malignant breast tumors were the length of hospital stay and age: 55-64 years vs. ≥65 years; JA25.Partial mastectomy for malignant breast tumor.Regarding the group without complications or comorbidities, the influencing factors for hospital costs were the length of hospital stay and age: 45–54 years, 55–64 years, and ≥65 years. Each factor had an impact on JA23.  breastThe impact of subtotal resection surgery for malignant tumors, including complications and associated conditions, on hospital costs was not statistically significant (P > 0.05). Conclusion Healthcare management departments should take appropriate measures.Develop localized and refined DRG cost-control tools that are in line with the local disease profile and actual economic levels. Optimize the structure of disease categories, reduce the length of hospital stays, and decrease consumables and medication costs.This will help reduce the costs associated with hospitalization for patients undergoing breast cancer surgery.

Objective Based on the analysis of DRGs combined with the workload of physicians in hospital settings, to examine the efficiency of bed utilization and the configuration status of different departments, in order to promote the refinement of public hospitals.This study provides theoretical support and decision-making assistance for managing hospital beds and optimizing the allocation of bed resources. Methodology A tertiary general hospital set up surgical beds in 2023.
Take the ward as an example. A Boston Matrix diagram is used to describe the types of bed utilization. The efficiency of departmental bed utilization is calculated based on the weights assigned to each bed, and it is also assessed in terms of the workload borne by each physician during their stay.Adjustments were made, and a comprehensive analysis was conducted of the status of bed allocation. Results Of the 27 surgical departments analyzed, there were 8 departments categorized as efficient, congested, idle, and cyclical.1, 5, 8, 6; the weight of each bed in 15 departments is higher than the average for surgical departments; the workload of each physician in 10 departments is greater than the average for surgical departments; beds.The departments with appropriate, insufficient, and excessive bed capacity numbers were 10, 8, and 9, respectively. Conclusion The calculation of theoretical bed capacity is influenced by the range of departments selected.The results derived from the departmental scope vary. By conducting a comprehensive analysis of bed utilization types, bed weighting, the workload of individual physicians during hospitalization, and the state of bed allocation, it is possible to effectively evaluate the situation.Evaluate the efficiency of hospital bed utilization while providing reference for continuous improvement of departments. Strengthen the coordination of management across the entire hospital, optimize bed allocation rules, regularly assess bed utilization efficiency, and establish a system for managing beds.Implement a dynamic adjustment mechanism to promote the optimal allocation of hospital beds. Accelerate the establishment and improvement of a hospital bed management information system to accurately reflect the operational efficiency of each available bed, thereby facilitating precision management.Refine the management of bed resources by providing the necessary data support.

Objective To investigate the impact of the implementation of a payment policy based on diagnosis-related groups (DRGs) on the clinical practices of urology departments in a tertiary medical institution Yes. Methodology A breakpoint regression model was employed to analyze the hospital’s urology department’s inpatient medical insurance billing data from 2020 to 2023, with a focus on DRG payments.The impact of the reform on diagnostic and treatment practices. Results The average medical expenses, average personal out-of-pocket expenses, average drug costs, and average treatment costs all significantly decreased after DRG payment, as did the average examination costs.The increase has been significant. Additionally, the reforms have notably increased the number of patient visits and reduced the length of hospital stays, although there has been no significant change in the CMI value. Conclusion DRG payment reform.It has a significant impact in controlling the growth of medical expenses, adjusting the structure of costs, and reducing the individual burden on patients. However, it cannot be ruled out that there may be instances of splitting hospital stays or admitting mild cases, which are covered by DRG payments.Adequate behavior.

Objective To investigate the relationship between the level of decision fatigue experienced by outpatient physicians and the inappropriate prescribing of antimicrobial agents, with the aim of developing targeted interventions to optimize antimicrobial use.The management of medications is supported by scientific evidence. Methods We selected outpatient prescription data from a tertiary general hospital in Yongzhou from September 2023 to August 2024 and incorporated it based on relevant literature.The top 3 categories of antimicrobial agents with the highest rates of inappropriate use were cephalosporins, macrolides, and quinolones. A total of 26,982 prescriptions were included. Data were collected through questionnaires.The levels of decision fatigue and basic information of 85 outpatient physicians were analyzed using a three-level random intercept logistic regression model to model the departments.doctor Nested prescription data, analysis underway.Examine the correlation between prescribing fatigue and the inappropriateness of prescriptions. Results The inappropriateness rate of targeted antimicrobial prescriptions was 16.55% (4465/26982), with a three-level logistic model.The regression analysis of TICS indicates that as the level of decision fatigue among outpatient physicians increases, the risk of inappropriate prescribing of antimicrobial agents also rises. Compared to the low fatigue group (DFS 0–6 points),The risk of inappropriate dosing is higher for the group with moderate fatigue (DFS 7–13 minutes) (OR = 1.49, 95% CI: 1.02–2.16), and even higher for the group with high fatigue (DFS 14–20 minutes).The risk of progression (OR = 1.83, 95% CI: 1.10–3.07) was highest in the group with extremely high fatigue (DFS 21–27 minutes); the OR for this group was 6.09, with a 95% CI of 2.24–16.54.His significant independent influencing factors included male patient gender (OR = 1.27, 95% CI: 1.18–1.37) and elderly patients (≥ 60 years old) (OR = 1.64, 95% CI:1.45 to 1.82), the type of medication was cephalosporins (OR = 3.30, 95% CI: 2.95 to 3.70) and macrolides (OR = 2.30, 95 % CI: 2.02 to 2.62).Conclusion The decision fatigue experienced by outpatient physicians is an independent risk factor for inappropriate prescribing of antimicrobial agents, and the greater the level of fatigue, the higher the risk of inappropriate antimicrobial prescribing.Increase. It is suggested that measures such as policy interventions at the departmental level and improvements to workflows be implemented to reduce the level of physician decision fatigue and enhance the quality of antibiotic prescriptions.

Objective This study aims to realize the full-chain connection of hospital rare disease data management, solve the difficulties in the collection and management of rare disease case data in the hospital through unified and standardized rare disease diagnosis across the hospital, so as to achieve refined management of rare diseases and provide solid data support for the development of rare disease undertakings in China. Methods The disease names in the rare disease catalog issued by the National Health Commission of the People's Republic of China were standardized in accordance with commonly used clinical medical terms and international classification of diseases standards, and then integrated into the clinical diagnosis dictionary database shared by the hospital outpatient and inpatient information systems. Subsequently, secondary development of the hospital information system was carried out based on the dictionary database to make it function in links such as case registration and reporting, functional department supervision, and disease data statistics. The rare disease cases in the third quarter of 2024 after the implementation of systematic supervision were selected as the observation group, and the rare disease cases in the second quarter of 2024 before the implementation of systematic supervision were selected as the control group. The reporting rates of the two groups were statistically analyzed. Results Based on the integration of the clinical diagnosis dictionary database and information system, the reporting rate of rare disease cases in the observation group (n=605) was 50.9% (308/605), which was significantly higher than 15.6% (174/1112) in the control group (n=1112), and the difference was statistically significant ($\chi^2$=241.280, P＜0.001). In addition, the application of the clinical diagnosis dictionary database increased the standardization rate of diagnostic names to 100%, and improved the efficiency of data statistics. Conclusion The application of the clinical diagnosis dictionary database has significantly increased the reporting rate of rare disease cases, improved the integrity of rare disease case reports and data accuracy in the hospital, laid a solid foundation for the construction of the hospital's rare disease management system, and become a key driving force for promoting the refined management of rare diseases in the hospital.

Objective To analyze the targeted monitoring results of nosocomial infections in the intensive care unit (ICU) of a hospital.Methods A retrospective analysis was conducted on the targeted monitoring data of ICU in a hospital from 2019 to 2023, including the nosocomial infection rate, distribution of nosocomial infection sites, utilization of three major catheters and related infections, and distribution of nosocomial infection pathogens.Results A total of 3 907 ICU patients were monitored within five years. The nosocomial infection rate was 6.86% and the case infection rate was 8.60%. There was a statistically significant difference in nosocomial infection rates among different years (\(P<0.05\)). The main infection site was the respiratory system, accounting for 66.67%. The incidence rates of catheter-associated urinary tract infection (CAUTI), catheter-related bloodstream infection (CRBSI) and ventilator-associated pneumonia (VAP) were 1.48‰, 0.66‰ and 6.51‰, respectively. A total of 296 pathogenic strains were isolated. The top five pathogens were Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Candida and Escherichia coli.Conclusion Continuous multi-year analysis of targeted monitoring data on ICU nosocomial infections reveals the epidemiological trend of nosocomial infections. It can provide an important basis for hospitals to formulate feasible prevention and control measures, and effectively reduce the incidence of nosocomial infections.

Objective To explore different configuration paths affecting the decline of cognitive function in community-dwelling elderly, and to provide references for early screening and intervention of cognitive decline in this population.Methods A convenience sampling method was adopted. Elderly residents aged 60 years and above in Binzhou City were investigated with a self-designed general information questionnaire and the Subjective Cognitive Decline Questionnaire (SCD-Q9). Qualitative comparative analysis was used to identify multiple configuration paths contributing to cognitive decline. Results A total of 127 elderly participants were included, among whom 40.16% reported subjective cognitive decline. Two major pathways leading to cognitive decline were identified. The first was a socially structural disadvantage pathway characterized by female gender, low educational level and rural residence. The second was a compound risk pathway based on structural disadvantages, combined with poor sleep quality and poor physical health. Conclusion Social structural disadvantages and adverse health conditions (including sleep problems) are crucial risk factors for cognitive decline in the elderly. Screening and intervention strategies should take the combined effects of multiple factors into account, with special attention to elderly rural women with low education.

Objective Traditional journal Impact Factor (IF) only focuses on citation counts. This study improved the conventional indicator by incorporating citation polarity to re-evaluate journal impact factors. Methods Two major SCI-indexed public health journals, Epidemiology and European Journal of Epidemiology, were selected as sample journals. Citations of papers published from 2019 to 2020 were analyzed. First, according to the citation purpose of citing articles, the citation polarity of cited publications was judged and manually labeled. Different weights were assigned to positive and negative citations relative to neutral citations. With consideration of citation polarity, three scoring criteria were adopted to calculate three types of revised citation frequencies. The re-evaluated impact factor (rIF) was further calculated, and the relative changes in journal impact factors after re-evaluation were compared between the two journals. Results A total of 532 cited articles were published by Epidemiology and European Journal of Epidemiology from 2019 to 2020, and 4 767 citation records were included for polarity analysis. Compared with the original citation counts, the coefficient of variation of the revised citation frequency increased in both journals. The original relative IF value of the two journals was 2.558, while the re-evaluated relative values rose to 2.596, 2.576 and 2.570 respectively. Conclusion Compared with the traditional IF that merely measures citation quantity, rIF provides a more comprehensive evaluation of journal academic influence with stronger discrimination.