Time points of outcome are often neglected in acupuncture meta-analyses: a methodological survey.

OBJECTIVES: To systematically understand the transparency of outcome measurement time point reporting in meta-analyses of acupuncture. STUDY DESIGN AND SETTING: We searched for meta-analyses of acupuncture published between 2013 and 2022 in PubMed, Embase, and Cochrane Library. A team of method-trained investigators screened studies for eligibility and collected data using pilot-tested standardized questionnaires. We documented in detail the reporting of outcome measurement time points in acupuncture meta-analyses. RESULTS: A total of 224 acupuncture meta-analyses were included. Of these, 98 (43.8%) studies did not specify the time points of primary outcome. Among 126 (56.3%) meta-analyses which reported the time points of primary outcome, only 22 (17.5%) meta-analyses specified time points in corresponding protocol. Among 48 (38.1%) meta-analyses that estimated treatment effects of multiple time points, 11 (22.9%) meta-analyses used inappropriate meta-analysis method (subgroup analysis) to pool effect size, and none of the meta-analyses used advanced methods for pooling effect sizes at different time points. CONCLUSION: Transparency in reporting outcome time points for acupuncture meta-analyses and appropriate methods to pool the effect size of multiple time points were lacking. For future systematic reviews, the transparency of outcome measurement time points should be emphasized in the protocols and final reports. Furthermore, advanced methods should be considered for pooling effect sizes at multiple time points.

Forecast of pain degree of lumbar disc herniation based on back propagation neural network.

To further explore the pathogenic mechanism of lumbar disc herniation (LDH) pain, this study screens important imaging features that are significantly correlated with the pain score of LDH. The features with significant correlation imaging were included into a back propagation (BP) neural network model for training, including Pfirrmann classification, Michigan State University (MSU) regional localization (MSU protrusion size classification and MSU protrusion location classification), sagittal diameter index, sagittal diameter/transverse diameter index, transverse diameter index, and AN angle (angle between nerve root and protrusion). The BP neural network training model results showed that the specificity was 95 ± 2%, sensitivity was 91 ± 2%, and accuracy was 91 ± 2% of the model. The results show that the degree of intraspinal occupation of the intervertebral disc herniation and the degree of intervertebral disc degeneration are related to LDH pain. The innovation of this study is that the BP neural network model constructed in this study shows good performance in the accuracy experiment and receiver operating characteristic experiment, which completes the prediction task of lumbar Magnetic Resonance Imaging features for the pain degree of LDH for the first time, and provides a basis for subsequent clinical diagnosis.

Physics-guided neural network for channeled spectropolarimeter spectral reconstruction.

A reconstruction method incorporates the complete physical model into a traditional deep neural network (DNN) is proposed for channeled spectropolarimeter (CSP). Unlike traditional DNN-based methods that need to employ training datasets, the method starts from randomly initialized parameters which are constrained by the CSP physical model. It iterates through the gradient descent algorithm to obtain the estimation of the DNN parameters and then to obtain the mapping relationship. As a result, it eliminates the need for thousands of sets of ground truth data, while also leveraging the physical model to achieve high-precision reconstruction. As seen, the physical model participates in the optimization process of DNN parameters, thus achieving physical guidance for the DNN output results. Based on the characteristic of the network, we designate this method as the physics-guided neural network (PGNN). Both simulations and experiments demonstrate the superior performance of the proposed method. Our approach will further promote the practical application of CSP in a wider range of fields.