Current formulation of objective performance criteria （OPC） in traditional Chinese medicine （TCM） faces methodological challenges， including poor reporting quality， insufficient standardization of methods， limited comparability of study populations， and inadequate consideration of the genetic and environment particularities of ethnic minority populations. In response to these issues， this paper proposes optimization strategies for setting OPC in TCM， with particular attention to ethnic minority medicine. These include improving methodological techniques and statistical descriptions， ensuring transparency in meta-analyses and data sources； standardizing the estimation of point estimates and confidence intervals， and incorporating multidisciplinary expert consensus for value determination； introducing local real-world data from the same period， especially cohort study evidence， to compensate for the lack of high-quality randomized controlled trial data. Through process transparency and dynamic updating mechanisms， the credibility of OPC method can be enhanced， thereby providing methodological support for strengthening evidence-based research capacity in this field.

Objective To analyze the optimization and clinical effectiveness of an intelligent triage-based priority healthcare information system for elderly outpatients.Methods A quasi-experimental design was a-dopted to develop priority queuing rules and implement a corresponding information system redesign for elder-ly patients aged≥80 years.Data from elderly outpatients before the implementation(January 1 to February 1,2024,n=4 017)were used as the control group,and data after the implementation(January 1 to February 1,2025,n=4 247)were used as the observation group.Waiting time was evaluated via check-in-to-call time intervals,supplemented by satisfaction surveys among nursing staff using a 5-point Likert scale.Re-sults Following the implementation of the priority outpatient system for elderly patients aged≥80 years,the observation group demonstrated a significant reduction in waiting time compared to the control group[163(50,467)s vs.746(140,2 362)s,P<0.001].Satisfaction surveys among outpatient nursing staff revealed an overall score of 4.91±0.29,with 91.9%of participants awarding the highest score of 5 points for functional satisfaction.Conclusion The priority outpatient system based on information system redesign significantly re-duces waiting times for patients aged≥80 years and improves satisfaction among nursing staff.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.

Objective:To establish a three-dimensional U-shaped residual coordinated attention network (URCA-Net) based on enhanced CT images for small bowel polyp detection and analyze its application effectiveness in intelligent detection of small bowel polyps.Methods:Abdominal CT data of patients with small bowel polyps were collected from the Air Force Medical Center between June 2019 and July 2023. All patients underwent bowel preparation followed by thin-slice spiral CT scanning to obtain enhanced CT arterial phase images. The data were randomly divided into training, validation and test sets in an 8∶1∶1 ratio. The URCA-Net deep learning model was used for small bowel polyp segmentation. The training set was used for model parameter training, the validation set for hyperparameter adjustment and monitoring of model generalization performance and the test set for final unbiased evaluation of the model. An early intelligent detection model for small bowel polyps was constructed, and its performance was evaluated. Evaluation metrics included pixel-level metrics for the segmentation task [Dice Similarity Coefficient (DSC)], as well as sensitivity and precision for polyp detection. A two-stage segmentation strategy was adopted: the first stage segmented the small bowel region to remove external interference, and the second stage performed polyp segmentation within the small bowel region.Results:A total of 78 subjects were included in the study, with an average age of (54±7) years. A total of 23 400 scan images were extracted, including 136 hyperplastic polyps, 298 hamartomatous polyps, 14 adenomatous polyps, and 4 cancerous polyps. On the test set, the average DSC for the first stage (small bowel segmentation) and the second stage (polyp segmentation) was 0.790 and 0.314, respectively. In the second stage task (polyp segmentation based on small bowel region), the polyp segmentation DSC increased to 0.701, with a precision of 0.836 (95% CI: 0.700-0.972) and a sensitivity of 0.759 (95% CI: 0.631-0.888) for polyp detection. Conclusion:The URCA-Net deep learning technique demonstrates good auxiliary diagnostic effectiveness in small bowel polyp detection and can provide a reference for screening and detection of small bowel polyps. The model is capable of generating high-quality segmentation results, which could facilitate evaluating polyp lesion morphology and provide support for downstream tasks such as preoperative navigation and risk prediction.

Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

Objective To construct the core outcome set(COS)for clinical research on Uyghur medicine in treating psoriasis by using internationally-recognized research methods,thus to aid the researchers in choosing the standardized outcomes and to enhance the quality of evidence from research findings.Methods A systematic retrieval of Chinese and English databases was conducted to collect outcomes from clinical studies,systematic reviews,and registered protocols of Uyghur medicine for treating psoriasis.An outcome pool was constructed based on the results of literature review.The outcomes were supplemented after qualitative surveys of patients and physicians,and then an initial list of outcomes was formed.Two rounds of Delphi surveys on the initial list were conducted for obtaining Likert scale ratings from different stakeholder groups to evaluate the importance of outcomes.After that,a consensus meeting was held to finalize the COS for clinical research on Uyghur medicine in treating psoriasis.Results The COS for clinical research on Uyghur medicine in treating psoriasis comprises six domains,totaling 14 outcomes.The 14 outcomes were psoriasis symptoms(psoriasis area and area severity index),skin manifestations(erythema,scaling,infiltration,etc.),investigator global assessment,patient global assessment,treatment satisfaction,health-related quality of life,recurrence rate,adverse events,drug adverse reactions,blood routine,urine routine,liver and kidney function,Uyghur medicine symptom assessment,and fluid changes.Conclusion The COS for clinical research on Uyghur medicine in treating psoriasis has been constructed,and will provide a reference for the selection of efficacy-evaluation outcomes and for the reporting of outcomes in related studies.

Objective To develop a core outcome set(COS)for treatment of progressive vitiligo in Uyghur medicine,and to standardize the selection and reporting of outcome measures in relevant studies.Methods Based on the existing core outcome domain set of randomized controlled trials for vitiligo,additional outcome indicators reflecting the advantages and characteristics of Uyghur medical treatment were developed.Specific indicators for Uyghur medical treatment of progressive vitiligo were collected through literature review and semi-structured questionnaire surveys,and then a list of indicators were formed.The Delphi survey and consensus meetings were used to select core indicators.Results A total of 54 studies were included,and 86 questionnaires were collected.Through literature review and questionnaire surveys,a list of 28 indicators were obtained.After two rounds of Delphi survey and one consensus meeting,12 outcome indicators in 7 domains were finally determined,including vitiligo lesion area,repigmentation,disease control time,maintenance of repigmentation,recurrence rate,immune indicators,psychological health,patients' quality of life,adverse events,adverse reaction incidence,liver and kidney function monitoring,and Uyghur medicine syndrome differentiation of mucus.Additionally,some measurement tools for certain indicators were recommended.Conclusion The development of the COS for vitiligo treatment in Uyghur medicine helps to comprehensively evaluate the efficacy of Uyghur medicine,and will provide a model for establishing efficacy evaluation methods that conform to the characteristics of ethnic minority medicine.

Objective:To establish a three-dimensional U-shaped residual coordinated attention network (URCA-Net) based on enhanced CT images for small bowel polyp detection and analyze its application effectiveness in intelligent detection of small bowel polyps.Methods:Abdominal CT data of patients with small bowel polyps were collected from the Air Force Medical Center between June 2019 and July 2023. All patients underwent bowel preparation followed by thin-slice spiral CT scanning to obtain enhanced CT arterial phase images. The data were randomly divided into training, validation and test sets in an 8∶1∶1 ratio. The URCA-Net deep learning model was used for small bowel polyp segmentation. The training set was used for model parameter training, the validation set for hyperparameter adjustment and monitoring of model generalization performance and the test set for final unbiased evaluation of the model. An early intelligent detection model for small bowel polyps was constructed, and its performance was evaluated. Evaluation metrics included pixel-level metrics for the segmentation task [Dice Similarity Coefficient (DSC)], as well as sensitivity and precision for polyp detection. A two-stage segmentation strategy was adopted: the first stage segmented the small bowel region to remove external interference, and the second stage performed polyp segmentation within the small bowel region.Results:A total of 78 subjects were included in the study, with an average age of (54±7) years. A total of 23 400 scan images were extracted, including 136 hyperplastic polyps, 298 hamartomatous polyps, 14 adenomatous polyps, and 4 cancerous polyps. On the test set, the average DSC for the first stage (small bowel segmentation) and the second stage (polyp segmentation) was 0.790 and 0.314, respectively. In the second stage task (polyp segmentation based on small bowel region), the polyp segmentation DSC increased to 0.701, with a precision of 0.836 (95% CI: 0.700-0.972) and a sensitivity of 0.759 (95% CI: 0.631-0.888) for polyp detection. Conclusion:The URCA-Net deep learning technique demonstrates good auxiliary diagnostic effectiveness in small bowel polyp detection and can provide a reference for screening and detection of small bowel polyps. The model is capable of generating high-quality segmentation results, which could facilitate evaluating polyp lesion morphology and provide support for downstream tasks such as preoperative navigation and risk prediction.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.