The success of implementation research is closely tied to the institution's pre-implementation readiness. This study aims to explore the organizational readiness for change (ORC) and its influencing factors on primary healthcare settings in the implementation of the "Shared Medical Appointment for Diabetes (SMART) in China: design of an optimization trial" and to enhance ORC and provide insights to support the effective implementation of the program.

ObjectiveTo explore the correlation between general anesthesia and mild cognitive impairment in older adults so as to provide new ideas for early prevention and timely intervention of mild cognitive impairment（MCI）. MethodsBased on the baseline survey of the Hubei memory and aging cohort study（2018-2023）， the participants completed a thorough neuropsychological assessment and physical examination， and self-reported a history of general anesthesia and surgery. The association of general anesthesia and MCI in the elderly was analyzed using the logistic regression model. In addition， the stratification and interaction analysis of anesthesia history， anesthesia number and physical intellectual exercise were conducted separately. ResultsA total of 5 069 older adults aged 65 and above were included in this study， including 3 692 city dwellers and 1 377 rural people， among whom were 2 584 women （51%）. Out of the 1 472 participants with history of general anesthesia， 249 people （17.4%） had MCI. After controlling for confounding factors， there was a 39.6% increased risk of MCI in older adults who underwent general anesthesia ［OR=1.396，95%CI（1.169，1.668），P<0.001］， suggesting that general anesthesia may be an independent influence on MCI. For the older adults who had one general anesthesia ［OR=1.235，95%CI（1.001，1.523），P=0.049］， two general anesthesia ［OR=1.779，95%CI （1.292，2.450），P<0.001］， and three OR more general anesthesia ［OR=2.395，95%CI （1.589，3.610），P<0.001］， their risks of MCI were increased by 23.5%， 77.9%， and 139.5%， respectively. Compared with the older adults without a history of general anesthesia who did not exercise， the risk of developing MCI was significantly negatively correlated with the exercise group， cognitive exercise group， and combined exercise and cognitive exercise groups （all P<0.001）. The risk of developing MCI in the exercise group was 60.2% of that in the no exercise group ［OR = 0.602， 95% CI（0.456， 0.795）］， the risk in the cognitive exercise group was 42.4% of that in the no exercise group ［OR = 0.424， 95% CI（0.294， 0.613）］， and the risk in the combined exercise and cognitive exercise group was 27.0% of that in the no exercise group ［OR = 0.270， 95% CI （0.208， 0.353）］. In the older adults with a history of general anesthesia， compared with the no exercise group， the risk of developing MCI was significantly negatively correlated with the cognitive exercise group and the combined exercise and cognitive exercise group （all P < 0.05）. The risk of developing MCI in the cognitive exercise group was 47.7% of that in the no exercise group ［OR=0.477， 95% CI （0.256，0.892）］， the risk in the combined exercise and cognitive exercise group was 34.5% of that in the no exercise group ［OR=0.345， 95% CI （0.220， 0.540）］， while the risk in the exercise-only group did not show a significant difference. ConclusionThe risk of MCI increased significantly in older adults with a history of general anesthesia， and this risk increased with the times of anesthesia. Physical and mental exercise reduces the risk of MCI. it is recommended that older adults with a history of anesthesia incorporate physical and mental exercise into their daily lives to prevent mild cognitive impairment.

This study aimed to localize the workplace readiness questionnaire (WRQ) and validate its applicability for assessing readiness for implementation of evidence-based practices (EBP) in primary care settings in China. The localization of the instrument will provide a practical instrument for assessing organizational readiness for change (ORC).

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Data analysis models may assist the transmission of traditional Chinese medicine （TCM） experience and clinical diagnosis and treatment， and the possibility of constructing a “data-knowledge” dual-drive model was explored by taking gastric precancerous state as an example. Data-driven is to make clinical decisions around data analysis， and its syndrome-differentiation decision-making research relies on hidden structural models and partially observable Markov decision-making processes to identify the etiology of diseases， syndrome elements， evolution of pathogenesis， and syndrome differentiation protocols； knowledge-driven is to make use of data and information to promote decision-making and action processes， and its syndrome-differentiation decision-making research relies on convolutional neural networks to improve the accuracy of local disease identification and syndrome differentiation. The “data-knowledge” dual-driven model can make up for the shortcomings of single-drive numerical simulation accuracy， and achieve a balance between local disease identification and macroscopic syndrome differentiation. On the basis of previous research， we explored the construction method of diagnostic assisted decision-making platform for gastric precancerous state， and believed that the diagnostic and decision-making ability of doctors can be extended through the assistance of machines and algorithms. Meanwhile， the related research methods were integrated and the core features of gastric precancerous state based on TCM syndrome differentiation and endoscopic pathology diagnosis and prediction were obtained， and the elements of endoscopic pathology recognition based on TCM syndrome differentiation were explored， so as to provide ideas for the in-depth research and innovative application of cutting-edge data analysis technology in the field of intelligent TCM syndrome differentiation.

Objective : To analyze the difference of urinary iodine level in Hashimoto thyroiditis ( HT) patients， and to explore the possible relationship between urinary iodine level and HT under different iodine nutritional sta- tus，so as to provide some references for reasonable iodine intake in HT patients． Methods : A total of 101 hospi- talized HT patients were selected as HT group and divided into 3 groups according to thyroid function : HT group with hyperthyroidism (41 cases) ．There were 25 cases in HT group with normal thyroid function．There were 35 cases in HT combined with hypothyroidism group．In addition，30 healthy subjects were selected as control group. Serum levels of thyroid stimulating hormone(TSH) ，triiodothyronine(T3 ) ，thyroxine (T4 ) ，thyroid peroxidase an- tibody (TPOAb) and thyroglobulin antibody (ATG) were detected by chemiluminescence assay．The size and mor- phological structure of thyroid organs were examined by ultrasonography．Urinary iodine was determined by catalytic spectrophotometry with arsenic and cerium．The nutritional status of iodine was classified into iodine deficiency ( < 100 μg/ L) ，iodine adequacy( 100 －199 μg/ L) ，iodine adequacy (200 －299 μg/ L) and iodine excess ( ≥ 300 μg/ L) ．Non-parametric test was used to compare urinary iodine level between HT group and control group，one- way ANOVA and t test were used to compare urinary iodine level between HT group and control group ，and Spearman correlation analysis was used to compare the correlation between urinary iodine level and T3 ，T4 ，TSH， ATG and TPOAb under different iodine nutrition status. Results : Compared with control group，ATG and TPOAb levels in HT group increased (P＜0. 001) ，and urinary iodine levels increased (P＜0. 05) ，with statistical signifi- cance．Compared with the control group in different thyroid function states，only the HT group with hypothyroidism increased the urinary iodine level (P＜0. 01) ，and the difference was statistically significant．Spearman correlation analysis showed that urine iodine level was positively correlated with ATG and TPOAb levels in iodine excess condi- tion (P＜0. 05) ，and urine iodine level was positively correlated with TSH level in iodine sufficient condition and iodine excess condition in HT patients (P＜0. 05) ． Conclusion The urinary iodine level of HT patients was high- er than that of normal people．When the urinary iodine level of residents is ≥ 300 μg/ L，iodine intake is prone to HT．When the urinary iodine level of HT patients is ≥ 200 μg/ L，iodine consumption is prone to hypothyroidism， and iodine intake should be limited.