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Assessing the risk of bias of clinical trials with large language models and ROBUST-RCT: a feasibility study
Abstract Risk of bias assessment is a crucial step in evidence synthesis. The traditionally adopted tool, however, is complex, resource-intensive, and unreliable. While prior investigations have focused on whether Large Language Models (LLMs) could perform assessments with RoB 2, this study is the first to evaluate the reliability of ROBUST-RCT, a novel risk-of-bias tool, as applied by humans and LLMs. Reviewers working independently used ROBUST-RCT to assess different aspects of a sample of RCTs and then reached a consensus through discussion. A chain-of-thought prompt instructed four LLMs on how to apply ROBUST-RCT. The primary analysis used Gwet’s AC2 to assess inter-rater reliability based on all the final ratings (i.e., the ratings in the second step of the tool) for all the core items of the ROBUST-RCT. A sample of 54 assessments, derived from 9 studies, was compared for each LLM against human consensus. In the primary analysis, Gwet’s AC2 inter-rater reliability varied across the LLMs. DeepSeek-R1, the lowest performer, yielded an AC2 of 0.46 ( 95% CI: 0.24 to 0.69). On the other side, Gemini 2.5 Pro Preview – the model with higher consistency with human consensus – yielded an AC2 of 0.69 (95% CI: 0.54 to 0.84). With 95% confidence, three of the four tested LLMs achieved ‘moderate’ or higher reliability based on benchmarking. LLMs could be helpful in the risk-of-bias assessment of systematic reviews using the ROBUST-RCT tool.
Mechanical mechanism of noise reduction performance of rubber granular asphalt mixture under freeze-thaw cycles
Lithium enrichment from mine waters using CO2 hydrate-based desalination
Abstract Lithium (Li) recovery from secondary resources, such as mine waters, is essential to meet the increasing global demand. However, lithium in mine waters typically occurs at low concentrations, necessitating efficient and environmentally sustainable methods for enrichment. Hydrate-based desalination (HBD) is a novel and energy-efficient approach that can simultaneously concentrate lithium and produce clean water. In this study, CO 2 HBD was applied to upgrade lithium in real mine waters. The effects of stirring rate and reaction time were evaluated, showing that operation at 600 rpm for 1 h achieved a lithium enrichment factor of 1.57 ± 0.09 and a water recovery of 53 ± 4%. A multi-stage configuration further improved performance, with Li enrichment in the brine stream reaching 2.75. This increased the concentration of lithium from 180 mg/L to about 500 mg/L, a level considered feasible for further treatment towards lithium recovery. The desalting efficiency in the hydrate phase also increased from 40 to 81%. A key finding was that naturally occurring fine particles (silicate/aluminosilicate particles) in the mine water acted as effective in situ kinetic promoters, eliminating the need for external additives. This not only simplifies the process design but also helps to avoid additional separation steps and costs. Overall, these results highlight CO 2 HBD as a promising technology for lithium enrichment from dilute aqueous resources and water reuse in mining operations, with potential to contribute to more sustainable resource management.
From chain length to cell death: mechanistic basis for ROS-mediated apoptosis induced by saturated fatty acids
Tierra: multi-tiered arrays and recency-aware hot data decision
Contrast enhancement for brain MRI images via genetic algorithm-based dual cut histogram equalization
A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans
Coupling MgSO4-assisted SALLE with a fluorimetric turn-off strategy for the determination of cinacalcet HCl in pharmaceutical and human matrices
ASCC3 promotes chemosensitivity in colorectal cancer cells
Blockchain-driven trust management and AI computing for sensor networks optimization
Investigation on the mechanical behavior of coconut leaf sheath and midrib of coconut leaf reinforced epoxy composites
Portable AI-powered scanning slit-light device for low-cost eye disease screening
Association between obstructive sleep apnea and male sexual dysfunction: a prospective cohort study based on 155,688 participants from the UK Biobank
Applying Liebig’s law of the minimum for variable rate soybean seeding based on CEC-nutrient fertility index
Long short-term attention memory (LSTAM): a global-feature-integrated model for joint moment prediction in human rehabilitation
Abstract Joint moments are critical parameters for evaluating human movement, and time-series models are widely used to predict them from biosignals. However, biosignals collected via accelerometers, gyroscopes, and electromyography (EMG) sensors are often susceptible to local features such as short-term fluctuations and noise, which hinders the models’ ability to effectively capture global features and weakens their capability to predict long-term trends. To address this issue, this paper proposes a long short-term attention memory (LSTAM) model that integrates global features. Our main contributions include the use of fast Fourier transform for spectral decomposition, multilayer perceptrons for nonlinear transformation, and convolutional modules to suppress the impact of local features in the sensor data. Additionally, an LSTM network enhanced with attention mechanisms is incorporated to dynamically focus on key temporal and frequency-domain patterns. We evaluated the proposed model on a publicly available dataset and compared its performance with existing methods, including LSTM, TCN, Conv2D, TimeMixer, xPatch, FFN, and TranSEMG. Experimental results show that the LSTAM model achieved a variance accounted for (VAF) of 0.907 ± 0.022 for hip flexion–extension (FE) and 0.927 ± 0.026 for hip abduction–adduction (AA); a root mean square error (RMSE) of 8.04 ± 2.27 (FE) and 5.56 ± 2.01 (AA); and a coefficient of determination (R 2 ) of 0.908 ± 0.029 (FE) and 0.922 ± 0.030 (AA). These results demonstrate that LSTAM significantly outperforms existing models, offering a robust and efficient solution for joint moment prediction and human rehabilitation evaluation.