Objective:To evaluate the effectiveness of T-piece resuscitator as a respiratory support strategy during the transport of critically ill premature infants,and to provide a scientific basis for clinical decision-making.Methods:A total of 280 critically ill premature newborns hospitalized in the NICU of Fifth Medical Center of Chinese People's Liberation Army General Hospital from January 2017 to December 2023 were included.Infants were categorized into three groups based on the respiratory support method given during transport: the ventilator group(108 cases),the T-piece group(102 cases),and the resuscitation sac group(70 cases).The transport distance,general condition at birth,prenatal conditions,dyspnea symptoms at admission,blood gas analysis results,clinical diagnosis,clinical intervations,and related treatment among the three groups were retrospectively analyzed.Results:There were no significant differences in the transport distance,the number of endotrached intubations during transport,the main complications during pregnancy,the general condition at birth,and the history of asphyxia among the three groups(all P>0.05).The incidence of triple-concave sign at admission in T-piece group was significantly lower than that in resuscitation sac group (41.7% vs.62.9%, P=0.005),and the arterial carbon dioxide tension(PaCO 2) at admission was also significantly lower in T-piece group than that in resuscitation sac group[(41.194±8.720) mmHg vs.(45.360±13.998) mmHg, P=0.034].Furthermore,the T-piece group had significantly lower rates of type II respiratory failure(0.9% vs.22.9%),respiratory acidosis(9.3% vs.27.1%),hypoxemia(7.4% vs.28.6%),hyperoxygen partial pressure(1.9% vs.28.6%),neonatal respiratory distress syndrome(66.7% vs.87.1%),and intracranial hemorrhage(18.5% vs.38.6%) during hospitalization compared to the resuscitation sac group (all P<0.05).The proportion of tracheal intubations(63.9% vs.87.1%) and the time of using non-invasive ventilator[1.0(1.0,2.0)d vs.1.0(1.0,6.0)d] were also significantly lower in T-piece group compared to the resuscitation sac group(both P<0.05).Compared with the respiratory group,there were no statistically significant differences in the aforementioned indicators for the T-piece group. Conclusion:The T-piece resuscitator can provide stable and adjustable positive end-inspiratory pressure and positive expiratory pressure,as well as a stable inspired oxygen flow rate,without increasing the risk of invasive procedures and severe complications.Its application during the transport and treatment of critically ill premature infants has definite clinical value.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Time perspective therapy can reshape the time perspective of chronic disease patients,improve their psychological state,optimize behavioral patterns,and enhance self-management awareness.This article reviews the concept and theoretical basis of time perspective therapy,assessment methods of time perspective,and influencing factors of time perspective in chronic disease patients,as well as methods and effects of applying time perspective therapy in self-management.Additionally,it analyzes challenges in implementation and proposes practical recommendations,aiming to provide psychological guidance for self-management interventions in chronic disease patients.

Objective To report cases of checkpoint inhibitor pneumonitis （CIP） in patients with advanced biliary tract cancer, aiming to provide additional approaches for the assessment, treatment, and monitoring of this condition. Methods Three patients developed oxygen desaturation and interstitial lung lesions during chemotherapy combined with immunotherapy, and were diagnosed with CIP in collaboration with the respiratory department. Antitumor therapy was discontinued in the acute phase, and glucocorticoids were administered, with regular monitoring of disease progression. During follow-up, case 1 developed lung metastasis; case 2 showed improvement; case 3 had concurrent infection and tumor progression. Results Glucocorticoids improved lung lesions and hypoxic symptoms in patients with CIP, but attention should be paid to the potential for concurrent infections and tumor progression. Conclusions Comprehensive assessment and early identification of CIP are crucial for patients with advanced biliary tract cancer. For those with recurrent symptoms after glucocorticoid therapy, timely and accurate adjustment of the treatment regimen is essential.

Objective:To evaluate the effectiveness of T-piece resuscitator as a respiratory support strategy during the transport of critically ill premature infants,and to provide a scientific basis for clinical decision-making.Methods:A total of 280 critically ill premature newborns hospitalized in the NICU of Fifth Medical Center of Chinese People's Liberation Army General Hospital from January 2017 to December 2023 were included.Infants were categorized into three groups based on the respiratory support method given during transport: the ventilator group(108 cases),the T-piece group(102 cases),and the resuscitation sac group(70 cases).The transport distance,general condition at birth,prenatal conditions,dyspnea symptoms at admission,blood gas analysis results,clinical diagnosis,clinical intervations,and related treatment among the three groups were retrospectively analyzed.Results:There were no significant differences in the transport distance,the number of endotrached intubations during transport,the main complications during pregnancy,the general condition at birth,and the history of asphyxia among the three groups(all P>0.05).The incidence of triple-concave sign at admission in T-piece group was significantly lower than that in resuscitation sac group (41.7% vs.62.9%, P=0.005),and the arterial carbon dioxide tension(PaCO 2) at admission was also significantly lower in T-piece group than that in resuscitation sac group[(41.194±8.720) mmHg vs.(45.360±13.998) mmHg, P=0.034].Furthermore,the T-piece group had significantly lower rates of type II respiratory failure(0.9% vs.22.9%),respiratory acidosis(9.3% vs.27.1%),hypoxemia(7.4% vs.28.6%),hyperoxygen partial pressure(1.9% vs.28.6%),neonatal respiratory distress syndrome(66.7% vs.87.1%),and intracranial hemorrhage(18.5% vs.38.6%) during hospitalization compared to the resuscitation sac group (all P<0.05).The proportion of tracheal intubations(63.9% vs.87.1%) and the time of using non-invasive ventilator[1.0(1.0,2.0)d vs.1.0(1.0,6.0)d] were also significantly lower in T-piece group compared to the resuscitation sac group(both P<0.05).Compared with the respiratory group,there were no statistically significant differences in the aforementioned indicators for the T-piece group. Conclusion:The T-piece resuscitator can provide stable and adjustable positive end-inspiratory pressure and positive expiratory pressure,as well as a stable inspired oxygen flow rate,without increasing the risk of invasive procedures and severe complications.Its application during the transport and treatment of critically ill premature infants has definite clinical value.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Time perspective therapy can reshape the time perspective of chronic disease patients,improve their psychological state,optimize behavioral patterns,and enhance self-management awareness.This article reviews the concept and theoretical basis of time perspective therapy,assessment methods of time perspective,and influencing factors of time perspective in chronic disease patients,as well as methods and effects of applying time perspective therapy in self-management.Additionally,it analyzes challenges in implementation and proposes practical recommendations,aiming to provide psychological guidance for self-management interventions in chronic disease patients.

Objective:To explore the anti-inflammatory effect and safety of two non-steroidal anti-inflammatory drugs in the phacoemulsification combined with intraocular lens (IOL) implantation.Methods:A randomized, double-blind, clinical trial was conducted.A total of 90 age-related cataract patients (90 eyes) who were diagnosed in Qingdao Eye Hospital Affiliated to Shandong First Medical University were enrolled from October 2020 to February 2021.The patients were randomized to diclofenac sodium group and bromofenac sodium group by random number table method, with 45 cases (45 eyes) in each group.All patients underwent phacoemulsification combined with IOL implantation, and 0.1% diclofenac sodium eye drops (preservative-free), 4 times a day, and 0.1% pramiphene eye drops, 2 times a day were applied in the perioperative period.The duration of continuous medication treatment and follow-up time were 6 weeks.The subjective symptoms of the patients were scored before and after surgery.The amount of tear fluid secretion was detected by Schirmer I test, and the tear film breakup time was recorded with the Oculus dry eye analyzer.Corneal fluorescein staining was observed under a slit lamp microscope with cobalt blue light.Anterior chamber flash was measured by slit-lamp biomicroscopy.The thickness of central macular area and the presence of macular cystoid edema was measured by optical coherence tomography.Visual acuity, noncontact intraocular pressure (IOP) and the drug safety were examined and evaluated.This study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Qingdao Eye Hospital (No.[2020]60).All patients were informed about the surgery and postoperative medication and signed the informed consent form.Results:All subjects had no intraoperative complications, and completed treatment and follow-up as required.The preoperative, 1-day postoperative, 1-week postoperative, 6-week postoperative subjective symptom scores were (0.47±0.73), (0.56±0.62), (0.33±0.48), and (0.51±0.66) points in the diclofenac group, and (0.47±0.51), (0.75±0.61), (0.64±0.65), and (0.78±0.77) points in the bromfenac group.There were statistically significant differences in the subjective symptom scores at different time points between the two groups ( Fgroup=5.001, P=0.028; Ftime=2.920, P=0.035), and the subjective symptom scores of diclofenac sodium group were significantly lower than those of bromofenac sodium group (all at P<0.05).The preoperative, 1-week postoperative, 6-week postoperative tear secretion volume were (5.87±2.37), (6.07±2.53), and (6.29±0.25) mm in diclofenac sodium group, and (7.36±2.74), (6.29±3.46), and (5.80±2.76) mm in bromofenac sodium group.There was statistically significant difference in the tear secretion volume between the two groups before surgery ( F=6.910, P=0.012), but there was no significant difference on postoperative weeks 1 and 6 ( F=1.121, 0.772; P=0.729, 0.384).The preoperative, 1-week postoperative, 6-week postoperative non-invasive tear break-up time (NIBUT) were (8.00±6.28), (6.68±5.24), and (6.17±5.00) seconds in diclofenac sodium group, and (6.40±5.28), (4.50±2.46), and (5.39±5.39) seconds in bromofenac sodium group.There was no significant difference in NIBUT between the two groups ( Fgroup=3.415, P=0.068).There was significant difference in NIBUT within groups among different time points ( Ftime=4.358, P=0.020).The 1-day postoperative, 1-week postoperative, 6-week postoperative corneal epithelial staining score were (1.40±0.81), (0.13±0.34), (0.00±0.00) points in diclofenac sodium group, and (1.38±0.89), (0.22±0.47), and (0.00±0.00) points in bromofenac sodium group.There was no statistically significant difference in the corneal epithelial staining score between the two groups after surgery ( Fgroup=0.110, P=0.741).There were statistically significant differences in corneal epithelial staining scores within groups among different time points ( Ftime=175.054, P<0.01).The 1-day postoperative, 1-week postoperative, 6-week postoperative anterior chamber flare classification were 1.13±0.51, 0.13±0.34, and 0.00±0.00 in diclofenac sodium group, and 1.02±0.34, 0.16±0.37, and 0.00±0.00 in bromofenac sodium group.There was no significant difference in the overall anterior chamber flash between the two groups ( Fgroup=0.045, P=0.507).There were statistically significant differences in anterior chamber flash within groups among different time points ( Ftime=322.331, P<0.001).There was no significant difference in the preoperative and 6-week postoperative macular fovea thickness between both groups ( t=-0.221, -0.374; both at P>0.05).The incidence of macular cystoid edema 6 weeks after operation was 0% in both groups.Subjects tolerated the two tested drugs well.Eight adverse events occurred in this study, all of which were mild postoperative IOP elevation, including 3 in diclofenac sodium group with an incidence of 6.67% and 5 in bromofenac group with an incidence of 11.1%.IOP returned to normal in all the patients 1 week after stopping the use of drug. Conclusions:Two nonsteroidal anti-inflammatory drugs are safe and effective for anti-inflammatory treatment after cataract phacoemulsification combined with IOL implantation.The new diclofenac sodium eye drops are more comfortable than bromfenac sodium eye drops.

Objective To establish a coronavirus(CoV)infection model using human nasal mucosa organoids for testing antiviral drugs and evaluate the feasibility of using human nasal mucosa organoids with viral infection as platforms for viral research and antiviral drug development.Methods Human nasal mucosa organoids were tested for susceptibility to SARS-CoV-2 and HCoV-OC43 pseudoviruses.In a P3 laboratory,nasal mucosa organoids were infected with the original strain of SARS-CoV-2 and 4 variant strains,and the infection conditions were optimized.The viral loads in the culture supernatants were measured at different time points using RT-qPCR,and immunofluorescence assay was employed to localize SARS-CoV-2 nucleocapsid protein to determine the type of the infected cells.In the optimized nasal mucosa viral infection model,the antiviral effects of camostat and bergamot extract(which were known to inhibit SARS-CoV-2)were tested and the underlying molecular mechanisms were explored.Results In the optimized nasal mucosa organoid models infected with SARS-CoV-2 and HCoV-OC43 pseudoviruses,the viral load in the culture supernatants increased significantly during the period of 2 to 24 h following the infection,which confirmed infection of the organoids by both of the pseudoviruses.The nasal mucosa organoids could be stably infected by the original SARS-CoV-2 strain and its 4 variant strains,validating successful establishment of the viral infection model,in which both camostat and bergamot extract exhibited dose-dependent antiviral effects.Conclusions Human nasal mucosa organoids with SARS-CoV-2 infection can serve as platforms for screening and testing antiviral drugs,particularly those intended for nasal administration.

Objective:To evaluate the reliability and validity of the Face, Legs, Activity, Cry, and Consolability (FLACC) Pain Behavioral Scale, the Children′s Hospital of Eastern Ontario Pain Scale (CHEOPS), and the Objective Pain Scale (OPS) during the general anesthesia recovery period in children with oral therapy, and to explore their screening ability for pain risk, so as to provide information for selecting appropriate pain assessment scales for pediatric patients.Methods:One hundred and four pediatric patients with oral therapy under general anesthesia were recruited at the Stomatological Hospital of Southern Medical University from January to May, 2024. Two researchers observed simultaneously and scored independently using three scales in random order at 15 minutes and 60 minutes after patients arrival at post anesthesia care unit (PACU). Those awake patients also used the Wong-Baker FACES Pain Rating Scale to report their pain. Internal consistency, inter-rater coefficient, construct and criterion validity of three scales were evaluated.Results:The final sample included 97 patients (50 males and 47 females), with an age of (4.88 ± 1.10) years. At 15 minutes and 60 minutes upon arrival at PACU, the Cronbach alpha coefficients for internal consistency of the FLACC, the CHEOPS, and the OPS were 0.993, 0.980, 0.990, and 0.991, 0.974, 0.989, respectively; the inter-rater correlation coefficients were 0.993, 0.985, 0.998, and 0.985, 0.984, 0.984, respectively; exploratory factor analysis extracted one factor from each scale, and cumulative variance contribution rates were 95.116%, 82.145%, 78.417%, and 89.706%, 67.652%, 75.978%, respectively. At 60 minutes upon arrival at PACU, the Spearman correlation coefficients between three scales and the Wong-Baker FACES Pain Rating scale were 0.621, 0.703, 0.588, respectively; Kappa coefficients of three scales were 0.608, 0.683, 0.520, and area under the ROC curve were 0.812, 0.839, 0.812, respectively.Conclusions:The three scales show good reliability and acceptable validity for assessing pain during the general anesthesia recovery period in children with oral therapy. The CHEOPS performs better in pain screening, followed by the FLACC, the OPS.