Comparative Efficacy of Pharmacological Therapies for Low Back Pain: A Bayesian Network Analysis.

Low back pain (LBP) is a common problem, but the efficacy of pharmacological therapies remains controversial. Therefore, we aimed to comprehensively evaluate and quantitatively rank various pharmacological therapies for patients with low back pain. Two meta-analyses were performed: an initial pair-wise meta-analysis, followed by network meta-analysis using a random-effects Bayesian model. We included randomized controlled trials comparing placebos, non-steroidal anti-inflammatory drugs, opioids, skeletal muscular relaxants, pregabalin (or gabapentin), and some drug combinations. The primary and secondary outcomes were pain intensity and physical function. Eighty-eight eligible trials with 21,377 patients were included. Here, we show that only skeletal muscle relaxants significantly decreased the pain intensity of acute (including subacute) low back pain. Several kinds of drugs significantly decreased the pain of chronic low back pain, but only opioids and cyclo-oxygenase 2-selective non-steroidal anti-inflammatory drugs effectively reduced pain and improved function. Pregabalin (or gabapentin) seemed to be an effective treatment to relieve pain, but it should be used with caution for low back pain.

A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom's Taxonomy of Cognition Objectives Perspective.

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners' cognitive level during online learning helps to monitor learners' cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom's taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.