Anterior cervical discectomy and fusion (ACDF) has been widely used in cervical spondylosis, but adjacent segment degeneration/disease (ASD) was inevitable. Cervical total disc replacement (TDR) could reduce the stress of adjacent segments and retard ASD in theory, but the superiority has not been determined yet. This analysis aimed that whether TDR was superior to ACDF for decreasing adjacent segment degeneration (ASDeg) and adjacent segment disease (ASDis). A meta-analysis was performed according to the guidelines of the Cochrane Collaboration with PubMed, EMBASE, Cochrane Library and CBM (China Biological Medicine) databases. It included randomized controlled trials (RCTs) that reported ASDeg, ASDis, and reoperation on adjacent segments after TDR and ACDF. Two investigators independently selected trials, assessed methodological quality, and evaluated the quality of this meta-analysis using the grades of recommendation, assessment, development, and evaluation (GRADE) approach. Eleven studies with 2632 patients were included in the meta-analysis. The overall rate of ASD in TDR group was lower than ACDF group (OR = 0.6; 95% CI [0.38, 0.73]; P < 0.00001). Both the incidence of ASDeg and the reoperation rate were statistically lower in the TDR group than in the ACDF group (OR = 0.58, P < 0.00001; OR = 0.52, P = 0.01, respectively). Subgroup analysis was performed according to the follow-up time and trial site; the rate of ASDeg was lower in patients underwent TDR no matter the follow-up time, and TDR tended to increase the superiority across time. The rate of ASDeg was also lower with TDR both in the USA and China (P < 0.0001, P = 0.03, respectively). But the cost-effectiveness result might be prone to neither of the two surgery approaches. According to GRADE, the overall quality of this meta-analysis was moderate. TDR decreased the rates of ASDeg and reoperation compared with that of ACDF, and the superiority may become more apparent over time. We cautiously and slightly suggest adopting TDR according to the GRADE but may not believe it excessively.

Background: The objective of this study was to systematically evaluate the safety and efficacy of local anesthesia, general anesthesia, and epidural anesthesia in percutaneous endoscopic lumbar discectomy (PELD).Methods: We searched PubMed, EMBASE, and OVID databases for all relevant studies. All statistical analysis was performed using STATA 17.0.Results: Fourteen studies were finally included, comprising 7 randomized controlled trials and 7 retrospective studies. The total number of subjects across these studies was 1655, with 316 undergoing general anesthesia, 789 undergoing local anesthesia, and 550 undergoing epidural anesthesia. The meta‐analysis of pairwise comparisons suggests that there are no differences among epidural, general anesthesia, and local anesthesia in terms of postoperative VAS, ODI, and surgery time. Regarding complications, general anesthesia has a higher complication rate compared with local anesthesia, but there are no differences between epidural and general anesthesia or between epidural and local anesthesia. In terms of anesthesia satisfaction, both general and epidural anesthesia have higher satisfaction rates compared with local anesthesia, with no significant difference between general and epidural anesthesia. The ranking of the best probabilities shows that epidural anesthesia has the lowest postoperative VAS and highest anesthesia satisfaction. General anesthesia has the lowest ODI scores. Local anesthesia has the fewest complications and operative time.Conclusion: Local anesthesia, general anesthesia, and epidural anesthesia are all safe and effective methods for PELD. Local anesthesia has advantages in complications and operation time. Epidural anesthesia is most advantageous in anesthesia satisfaction and postoperative VAS scores. General anesthesia is most advantageous in postoperative ODI. In the future, more multicenter RCTs are needed to further compare the safety and effectiveness of different anesthesia methods in PELD.

To develop machine-learning model that combines pre-operative vertebral-body CT radiomics with clinical data to predict cage subsidence after single-level ACDF with Zero-P. We retrospectively review 253 patients (2016–2023). Subsidence is defined as ≥ 3 mm loss of fused-segment height at final follow-up. Patients are split 8:2 into a training set (n = 202; 39 subsidence) and an independent test set (n = 51; 14 subsidence). Vertebral bodies adjacent to the target level are segmented on pre-operative CT, and high-throughput radiomic features are extracted with PyRadiomics. Features are z-score–normalized, then reduced by variance, correlation and LASSO. Age, vertebral Hounsfield units (HU) and T1-slope entered a clinical model. Eight classifiers are tuned by cross-validation; performance is assessed by AUC and related metrics, with thresholds optimized on the training cohort. Subsidence patients are older, lower HU and higher T1-slope (all P < 0.05). LASSO retained 11 radiomic features. In the independent test set, the clinical model had limited discrimination (AUC 0.595). The radiomics model improved performance (AUC 0.775; sensitivity 100%; specificity 60%). The combined model is best (AUC 0.813; sensitivity 80%; specificity 80%) and surpassed both single-source models (P < 0.05). A pre-operative model integrating CT-based radiomic signatures with key clinical variables predicts cage subsidence after ACDF with good accuracy. This tool may facilitate individualized risk stratification and guide strategies—such as endplate protection, implant choice and bone-quality optimization—to mitigate subsidence risk. Multicentre prospective validation is warranted.

This study explores the use of radiomic features extracted from preoperative T2-weighted MRI and CT images, combined with machine learning models, to predict the risk of vertebral refracture after percutaneous kyphoplasty (PKP) in postmenopausal women. We retrospectively collect data from 156 postmenopausal women with osteoporotic vertebral compression fractures (OVCFs) who underwent PKP (35 refracture cases, 121 non-refracture controls). All patients had preoperative lumbar T2-weighted MRI and CT scans. We extract MRI and CT radiomic features and constructed radiomic signatures through feature selection. Key clinical factors (age, body mass index [BMI], vertebral CT Hounsfield unit [HU] values, smoking history, diabetes history, alcohol use, etc.) are used to build clinical prediction models. Various machine learning classifiers (Support Vector Machine [SVM], K-Nearest Neighbors [KNN], Random Forest [RF], ExtraTrees, XGBoost, LightGBM, Multi-layer Perceptron [MLP]) are trained on the radiomic signatures and clinical factors. Model performance was evaluated on an independent test set using area under the ROC curve (AUC) as the primary metric. Accuracy, sensitivity, specificity, and other measures on the test set were compared between radiomic models, clinical models, and a combined model. The refracture group (n = 35, 22.4%) is significantly older (72.09 ± 4.25 vs 70.11 ± 3.31 years, P = 0.002) with lower vertebral bone density (97.00 ± 6.31 vs 102.49 ± 4.68 HU, P < 0.001). Among individual algorithms, the KNN clinical model achieves optimal performance (AUC = 0.74), while the SVM radiomics model demonstrates the best accuracy (AUC = 0.798, accuracy = 0.839, sensitivity = 0.857, specificity = 0.833). The combined model achieves superior performance (AUC = 0.886), significantly outperforming both standalone models. Multi-modal radiomics combined with key clinical factors provides superior prediction of refracture risk after PKP. This approach offers clinicians an objective tool for individualized risk stratification, representing a meaningful step toward precision medicine in managing osteoporotic fractures.