Jiangsu Provincial People's Hospital takes the reform of DRG payment method as an opportunity,based on the theory of incentive behavior,uses literature research,expert consultation,and key performance indicator methods to develop evaluation indicators,and applies PDCA management tools to establish a continuously improving medical insurance service quality evaluation system.It introduces the process of medical insurance service quality evaluation system construction and its application in medical insurance performance management,and analyzes the implementation effect:DRG operation is improving,disease group structure is optimized,medical quality and efficiency continue to improve,and medical service evaluation scores are improving.

Objective:To investigate the application effect of case-based collaborative learning (CBCL) based on data-information-knowledge-wisdom (DKIW) model in the training of the informatization teaching ability of clinical teachers.Methods:From March to August in 2022, 71 clinical teachers from four grade A tertiary hospitals in Tianjin, China, were selected as subjects and were randomly divided into control group with 35 patients and experimental group with 36 patients using a random number table. The teachers in the control group received blended teaching online and offline, and those in the experimental group received CBCL teaching based on DIKW model. The two groups were compared in terms of theoretical assessment score, informatization teaching demonstration score, and informatization teaching ability score before and after intervention. SPSS 27.0 was used for the t-test and the Mann-Whitney U rank sum test. Results:Compared with the control group after intervention, the experimental group had significantly higher scores of theoretical assessment (83.50±3.11) and informatization teaching demonstration (84.19±1.89) ( P<0.05). After intervention, the control group had significant increases in the total score of informatization teaching ability (74.34±4.08) and the scores of each dimension (15.40±1.19, 19.29±1.62, 28.54±1.67, and 11.11±1.79), and the experimental group also had significant increases in the total score of informatization teaching ability (83.64±5.25) and the scores of each dimension (16.53±1.21, 20.94±1.98, 33.03±2.10, and 13.14±1.48); the experimental group had significantly higher scores than the control group ( P<0.05). Conclusions:The CBCL teaching model based on DIKW model can help to improve the comprehensive informatization teaching ability of clinical teachers.

Objective To analyze the influencing factors of clinical pregnancy and live birth rates in patients undergoing frozen-thawed embryo transfer(FET)for the first time.Methods The clinical data of 1 458 patients who underwent FET cycle-assisted pregnancy for the first time were retrospectively analyzed and divided into four groups according to clinical pregnancy and live bith outcomes.The clini-cal data were compared to analyze the factors affecting clinical pregnancy and live birth rates in FET cycles that were included in multiple logistic regression analysis.Results Of the 1458 cycles,the clinical pregnancy and live birth rates were 44.0% and 34.0%,respectively.The mean age of the clinical pregnancy and live birth groups was lower than that in non-clinical pregnancy and stillbirth groups(P<0.05).The clinical pregnancy and live birth rates of patients aged<35 years were higher than those aged≥35 years(P<0.05).The clinical preg-nancy and live birth rates of patients with≥8 mm endometrial thickness were higher than those with<8 mm endometrial thickness(P<0.05).The clinical pregnancy rate of natural cycles of endometrial preparation regimen was higher than that of HRT cycles(P<0.05).The clinical pregnancy and live birth rates of double-embryo transfers were higher than that of single-embryo transfers(P<0.05).The clinical pregnancy and live birth rates of blastocyst transfers were higher than those of cleavage stage(P<0.05).Conclusion Age,endometrial thickness,number of transplanted embryos,and embryo morphology were the independent factors influencing clinical pregnancy and live birth outcomes during FET cycle transplantation.

Objective:To investigate the risk factors for severe distal curve progression after posterior hemivertebra (HV) resection and short-segment fixation in patients with congenital cervicothoracic scoliosis (CTS), and to analyze the surgical revision strategy.Methods:Imaging and clinical data of patients who underwent posterior HV resection and short-segment fixation for CTS between August 2012 and August 2021 at Nanjing Drum Tower Hospital were retrospectively analyzed. A total of 55 patients were recruited, including 27 females and 28 males with an average age of 8.5±3.6 years (range 3-15 years) at surgery and an average Risser grade of 0.7±1.4 (range 0-4). The number of fused segments averaged 6.9±1.6 (range 4-10), and the mean follow-up was 38.7±18.9 months (range 9-94 months). According to the severity of distal curve progression, the recruited patients were divided into three groups: non-progression group (NPG), mild progression group (MPG), and severe progression group (SPG). The latter two groups were collectively called the progression group (PG). The cervicothoracic Cobb angle, T1 tilt angle, coronal balance distance (CBD), neck tilt angle, clavicular angle, head tilt angle, head shift, and upper (UIV) and lower instrument vertebra (LIV) tilt angle on the standing whole spine X-ray were measured before and after surgery and at the last follow-up. The correction rate of the Cobb angle in the osteotomy area was measured and calculated on CT three-dimensional reconstruction, and the proportion of patients with Klippel-Feil syndrome (KFS) was recorded. Statistical analysis was conducted on the various parameters between the two groups. For factors with statistical significance in the single-factor analysis, binary logistic regression analysis was performed to identify the high-risk factors for distal curve progression.Results:There were 38 cases in the NPG, 11 in the MPG, and 6 in the SPG. Compared to the NPG, the PG showed more severe coronal imbalance preoperatively, with CBD of 35.6±22.3 mm and 11.6±7.1 mm respectively; more severe neck tilt and head shift, with neck tilt angle of 17.4°±8.3° and 12.4°±6.9° respectively, and head shift of 22.8±17.7 mm and 13.9±9.8 mm respectively; and a higher proportion of KFS, 65% (11/17) and 34% (13/38) respectively, all with statistical significance ( P<0.05). Postoperatively, the PG showed more severe coronal imbalance compared with the NPG, with 17.3±12.7 mm and 9.6±8.1 mm respectively; more evident residual deformity, with cervical tilt angles of 9.4°±4.6° and 6.4°±5.3° respectively, and head shift of 14.7±7.4 mm and 9.1±5.9 mm respectively; lower correction of Cobb angle in the apical osteotomy region, with rates of 40.1%±15.2% and 50.3%±19.9% respectively; more significant UIV and LIV tilt, with UIV tilt angles of 14.3°±7.4° and 9.8°±5.3° respectively, and LIV tilt angles of 8.1°±5.5° and 4.5°±3.6° respectively, all with statistical significance ( P<0.05). SPG showed only more severe coronal imbalance preoperatively compared with the MPG, with 50.7±31.3 mm and 27.3±9.6 mm respectively; and head shift, with 33.5±25.0 mm and 16.9±11.0 mm respectively, all with statistical significance ( P<0.05). Logistic regression analysis demonstrated a significant correlation between significant preoperative coronal imbalance and postoperative distal scoliosis progression [ OR=1.299, 95% CI (1.101, 1.531), P=0.002]. Five cases (83.3%) in SPG underwent revision surgery with an average follow-up of 25 months, and selecting the LIV down to the stable region was the major revision strategy. Conclusion:Combined KFS, residual cervicothoracic deformities, and tilting of UIV and LIV are key causes, whereas significant preoperative coronal imbalance is an independent risk factor predisposing to the distal curve progression.

Objective:To compare the clinical outcomes between three-column osteotomy and posterior-column osteotomy for correcting dystrophic kyphoscoliosis secondary to neurofibromatosis type 1 (DKS-NF1).Methods:ALL of 84 patients with DKS-NF1 were retrospectively analyzed, and the average age was 17.7±6.9 years. There were 50 cases with single curve, 18 cases with double curves, and 16 cases with triple curves; kyphosis was found in 42 cases in the thoracic area, 31 cases in the thoracolumbar area, and 11 cases in the lumbar area. The patients were divided into two groups: posterior column osteotomy group and three column osteotomy group based on surgical strategy. The radiographic parameters (including the magnitude of kyphosis, scoliosis, coronal balance distance, etc.) were compared between the two groups before and after surgery, and during the follow-up. The surgical efficacy was also compared based on the spinal correction and complications (such as cerebrospinal fluid leakage, pneumothorax, rod breakage, etc.).Results:The posterior column osteotomy group consisted of 74 patients and the column osteotomy group consisted of 10 patients. The age of patients in the posterior column osteotomy group was significantly younger than that in the three-column osteotomy group (15.8±4.8 years vs. 29.4±10.2 years, t=7.088, P<0.001), and the proportion of preoperative traction in this group was significantly higher than that in the three column osteotomy group (26/74 vs. 0, P=0.027). The apex of kyphosis in the three-column osteotomy group mainly located in the thoracolumbar and lumbar area, significantly higher than that in the posterior column osteotomy group (10/10 vs. 32/74, P=0.001). The magnitude of kyphosis in the two groups were 73.8°±20.9° and 63.1°±21.4° before surgery, respectively ( t=1.506, P=0.136). After surgery, they were corrected to 43.1°±20.9° and 21.1°±22.8°, respectively ( t=3.066, P=0.003), with correction rates of 43.7% ±19.6% and 84.1% ±78.7%, respectively ( t=3.677, P<0.001). At the last follow-up, they were maintained at 46.5°±20.9° and 24.6°±25.5°, respectively ( t=3.016, P=0.003). The Cobb angle of the main curve was corrected from preoperative 83.0°±29.0° and 66.3°±17.7° ( t=1.766, P=0.081) to postoperative 50.6°±20.8° and 40.8°±15.6° ( t=1.436, P=0.155), with correction rates of 38.3% ±16.6% and 39.3% ±12.7% ( t=0.191, P=0.849), respectively. At the last follow-up, they were maintained at 52.3°±20.5° and 43.1°±18.2°, respectively ( t=1.339, P=0.185). The proportion of multi-rod system application and screw density in three column osteotomy group was significantly higher than that in posterior column osteotomy group (8/10 vs. 20/74, P=0.002; 72.0% ±11.3% vs. 61.4% ±14.6%, t=2.173, P=0.033). The incidence of complications in the two groups was 12.2% (posterior column osteotomy group, 9/74) and 20% (three column osteotomy group, 2/10), respectively, with no statistically significant difference ( P=0.613). Conclusion:Three-column osteotomy is mainly used to treat adult kyphosis in DKS-NF1 patients. While the posterior column osteotomy methods were mainly applied in young patients. Most patients can achieve the purpose of deformity correction by posterior column osteotomy alone or combined with anterior complementary fusion. For patients with severe kyphosis, preoperative Halo gravity traction can help to further correct the intraoperative deformities.

Phytoremediation plays an important role in the treatment of heavy metal pollution in soil. In order to elucidate the mechanism of salicylic acid (SA) on copper absorption, seedlings from Xuzhou (with strong Cu-tolerance) and Weifang Helianthus tuberosus cultivars (with weak Cu-tolerance) were selected for pot culture experiments. 1 mmol/L SA was sprayed upon 300 mg/kg soil copper stress, and the photosynthesis, leaf antioxidant system, several essential mineral nutrients and the changes of root upon copper stress were analyzed to explore the mechanism of copper resistance. The results showed that Pn, Tr, Gs and Ci upon copper stress decreased significantly compared to the control group. Meanwhile, chlorophyll a, chlorophyll b and carotenoid decreased with significant increase in initial fluorescence (F₀), maximum photochemical quantum yield of PSⅡ (Fv/Fm), electron transfer rate (ETR) and photochemical quenching coefficient (qP) content all decreased. The ascorbic acid (AsA) content was decreased, the glutathione (GSH) value was increased, the superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX) activity in the leaves were decreased, and the peroxidase (POD) activity was significantly increased. SA increased the Cu content in the ground and root system, and weakened the nutrient uptake capacity of K, Ca, Mg, and Zn in the root stem and leaves. Spray of exogenous SA can maintain the opening of leaf stomata, improve the adverse effect of copper on photosynthetic pigment and PSⅡ reaction center. Mediating the SOD and APX activity started the AsA-GSH cycle process, effectively regulated the antioxidant enzyme system in chrysanthemum taro, significantly reduced the copper content of all parts of the plant, and improved the ion exchange capacity in the body. External SA increased the content of the negative electric group on the root by changing the proportion of components in the root, promoted the absorption of mineral nutrient elements and the accumulation of osmoregulatory substances, strengthened the fixation effect of the root on metal copper, and avoided its massive accumulation in the H. tuberosus body, so as to alleviate the inhibitory effect of copper on plant growth. The study revealed the physiological regulation of SA upon copper stress, and provided a theoretical basis for planting H. tuberosus to repair soil copper pollution.
Antioxidants ; Copper ; Helianthus/metabolism* ; Salicylic Acid/pharmacology* ; Chlorophyll A/pharmacology* ; Spectroscopy, Fourier Transform Infrared ; Chlorophyll/pharmacology* ; Ascorbic Acid ; Superoxide Dismutase/metabolism* ; Photosynthesis ; Glutathione ; Plant Leaves ; Stress, Physiological ; Seedlings

Objective:To construct a prediction model of psychological distress risk in young and middle-aged patients with gynecologic malignancy based on random forest algorithm and validate its prediction effect, which provided a tool for healthcare professionals to detect patients′ psychological distress in early stage.Methods:This was a cross-sectional study, a total of 385 cases of young and middle-aged patients with gynecologic malignancies admitted to the gynecology and oncology departments of six tertiary hospitals in Tianjin from October 2021 to October 2022 were consecutively included, the study subjects were randomly divided into 270 cases in the training set and 115 cases in the testing set according to 7:3 by R-studio software. After grouping the training set patients according to the presence or absence of psychological distress (positive psychological distress 151 cases and negative psychological distress 119 cases), univariate analysis was performed on each influencing factor. A random forest model for the prediction of psychological distress in young and middle-aged gynecological malignancy patients using R-studio software on the training set, and the prediction effect was verified on the testing set.Results:The prediction accuracy was 94.78%, sensitivity was 96.88%, specificity was 92.16%, positive predictive value was 93.94%, negative predictive value was 95.92%, and AUC was 0.992 (95% CI 0.982-1.000). The top 5 significant predictor variables were ranked according to the average decrease in the Gini coefficient of each influencing factor in the random forest model: General Self-Efficacy Scale score, Herth Hope Index score, Perceived Social Support Scale score, Self-Rating Depression Scale score, Self-Rating Anxiety Scale score. Conclusions:In this study, the prediction model of psychological distress in young and middle-aged patients with gynecologic malignancy constructed by random forest algorithm has high predictive efficacy, which provides reference for healthcare professionals to identify patients′ psychological distress early and formulate interventions.

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning