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BACKGROUND: Altmetric Attention Score (AAS) captures online attention received by a research article in addition to traditional bibliometrics. We present a comprehensive bibliometric analysis of high AAS articles and identify predictors of AAS in orthopedics. MATERIALS AND METHODS: The top 30 articles with highest AAS were selected from orthopedic journals using the Dimensions App. Multilevel mixed-effects linear regression was used to address clustering in articles from the same journal, with journals as the leveling variable. RESULTS: A total of 750 articles from 25 journals were included. In the final multivariable model, the funding source (none, industry, government, foundation, university, or multiple), findings (positive, negative, neutral, or not applicable), and the journal's impact factor were significant at P<.05. CONCLUSION: Predictors of AAS are similar to predictors of traditional bibliometrics. Future studies need prospective dynamic data to further elucidate the AAS. [Orthopedics. 20XX;4X(X):XXX-XXX.].
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Fiber-reinforced polymers (FRPs) are increasingly being used as a composite material in concrete slabs due to their high strength-to-weight ratio and resistance to corrosion. However, FRP-reinforced concrete slabs, similar to traditional systems, are susceptible to punching shear failure, a critical design concern. Existing empirical models and design provisions for predicting the punching shear strength of FRP-reinforced concrete slabs often exhibit significant bias and dispersion. These errors highlight the need for more reliable predictive models. This study aims to develop gradient-boosted regression tree (GBRT) models to accurately predict the shear strength of FRP-reinforced concrete panels and to address the limitations of existing empirical models. A comprehensive database of 238 sets of experimental results for FRP-reinforced concrete slabs has been compiled from the literature. Different machine learning algorithms were considered, and the performance of GBRT models was evaluated against these algorithms. The dataset was divided into training and testing sets to verify the accuracy of the model. The results indicated that the GBRT model achieved the highest prediction accuracy, with root mean square error (RMSE) of 64.85, mean absolute error (MAE) of 42.89, and coefficient of determination (R2) of 0.955. Comparative analysis with existing experimental models showed that the GBRT model outperformed these traditional approaches. The SHapley Additive exPlanation (SHAP) method was used to interpret the GBRT model, providing insight into the contribution of each input variable to the prediction of punching shear strength. The analysis emphasized the importance of variables such as slab thickness, FRP reinforcement ratio, and critical section perimeter. This study demonstrates the effectiveness of the GBRT model in predicting the punching shear strength of FRP-reinforced concrete slabs with high accuracy. SHAP analysis elucidates key factors that influence model predictions and provides valuable insights for future research and design improvements.
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Ovarian cancer, being one of the prevalent gynecological cancers, warrants a therapy that's both effective and well tolerated. After extensive drug testing, combination regimens with paclitaxel plus platinum-based agents such as cisplatin/carboplatin and taxanes, have shown promising results for advanced ovarian cancer. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to compare the efficacy of two treatment regimens for advanced ovarian cancer: cisplatin/paclitaxel and carboplatin/paclitaxel. PubMed (Medline), Science Direct, and Cochrane Library were searched from inception to March 2023. The meta-analysis included patients with histologically verified International Federation of Gynaecology and Obstetrics (FIGO) stages IIB to IV ovarian carcinoma who received either carboplatin/paclitaxel or cisplatin/paclitaxel. The primary outcomes were progression-free survival (PFS), overall survival (OS), quality of life (QOL), complete response rate (CRR), and partial response rate (PRR). The revised Cochrane Risk of Bias Tool 2.0 was used to assess the quality of the RCTs The five RCTs chosen for this statistical analysis consisted of a total of 2239 participants, with 1109 receiving paclitaxel/cisplatin for treatment and the remaining 1130 receiving carboplatin/paclitaxel. Among all included outcomes, these reported significant findings: QoL (p-value=0.0002), thrombocytopenia (p=<0.00001), neurological toxicity (p-value=0.003), nausea/vomiting (p-value=<0.00001), myalgia/arthralgia (p-value=0.02), and febrile neutropenia (p-value=0.01). We concluded that the carboplatin/paclitaxel doublet endows a better quality of life (QOL) to patients along with significantly fewer gastrointestinal and neurological toxicities when compared with the cisplatin/paclitaxel combination. However, the myelosuppressive effects of carboplatin/paclitaxel remain a point of concern and may require clinical management.
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Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete's environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements.
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A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R2) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that-when compared to the default GBRT model-the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R2 values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach.