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Background Clinical practice guidelines in primary care are important tools to guide primary care practice, but they are found with relatively low quality and unstandardized reporting on the whole. Objective To develop the reporting guideline for clinical practice guidelines in primary care based on the Reporting Items for practice Guidelines in HealThcare (RIGHT) . Methods According to the reporting methods recommended by EQUATOR Network, an item development group and an expert consensus group were established to develop reporting guideline for clinical practice guidelines in primary care. Content analysis, brainstorming, and topic analysis were used in developing the initial items. The Delphi survey was conducted to determine the final items. Results Thirteen items (five were modified and eight were added) were developed initially, covering five domains: guideline title, health issues, intended setting, recommendations and suggestions for future research. Then the initial item checklist was revised to a final version consisting of 15 items according to the results of two rounds of Delphi survey (with 100% response rates from 22 experts) conducted from December 2 to December 26, 2022. Conclusion This reporting checklist will provide a detailed guide for developers of guidelines in primary care, which will further improve the reporting quality of guidelines in primary care, especially that of those in China.

Steel surface defects have a significant impact on the quality and performance of many industrial products and cause huge economic losses. Therefore, it is meaningful to detect steel surface defects in real time. To improve the detection performance of steel surface defects with variable scales and complex backgrounds, in this paper, a novel method for detecting steel surface defects through a multiscale local and global feature fusion mechanism is proposed. The proposed method uses a convolution operation with a downsampling mechanism in the convolutional neural network model to obtain rough multiscale feature maps. Then, a context-extraction block (CEB) is proposed to adopt self-attention learning on the feature maps extracted by the convolution operation at each scale to obtain multiscale global context information to make up for the shortcomings of convolutional neural networks (CNNs), thus forming a novel multiscale self-attention mechanism. Afterwards, using the feature pyramid structure, multiscale feature maps are fused to improve multiscale object detection. Finally, the channel and spatial attention module and the WIOU (Wise Intersection over Union) loss function are introduced. The model achieved 78.2% and 71.9% mAP respectively on the NEU-DET and GC10-DET dataset. Compared to algorithms such as Faster RCNN and EDDN, this method is effective in improving the detection performance of steel surface defects. [ABSTRACT FROM AUTHOR]

The use of convolutional neural networks (CNN) for crowd counting has made significant progress in recent years; however, effectively addressing the scale variation and complex backgrounds remain challenging tasks. To address these challenges, we propose a novel Multi-Scale Guided Self-Attention (MSGSA) network that utilizes self-attention mechanisms to capture multi-scale contextual information for crowd counting. The MSGSA network consists of three key modules: a Feature Pyramid Module (FPM), a Scale Self-Attention Module (SSAM), and a Scale-aware Feature Fusion (SFA). By integrating self-attention mechanisms at multiple scales, our proposed method captures both global and local contextual information, leading to an improvement in the accuracy of crowd counting. We conducted extensive experiments on multiple benchmark datasets, and the results demonstrate that our method outperforms most existing methods in terms of counting accuracy and the quality of the generated density map. Our proposed MSGSA network provides a promising direction for efficient and accurate crowd counting in complex backgrounds. [ABSTRACT FROM AUTHOR]