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  1. 1661

    EVALUATION OF WORLD ENERGY SCENARIOS by V. I. Salygin, I. I. Litvinyuk

    Published 2016-04-01
    “…Therefore, nowadays a number of experts are exploring driving forces, critical uncertainties and unknowns that have an influence on future energy systems development. …”
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    Article
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    Development and validation of the pain management competency scale for nurses and a nationwide survey of Chinese nurses by Yixue Wu, Xiang Pan, Lihui Gu, Yingge Tong, Siyi Chen, Ke Ni, Hangyan Du, Meng Yang, Jianyi Wang, Yi Chen, Yeling Wei, Lingling Luo, Wenqian Cheng

    Published 2025-01-01
    “…Methods An item pool and a draft scale were developed through literature reviews, semi-structured interviews, and expert panel discussions. This was followed by refinement of the scale via Delphi expert consultations and a pilot test. …”
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    An Evidence summary of postoperative pain management in spinal surgery (脊柱外科术后患者疼痛管理的证据总结) by XIANG Tingting (向婷婷), ZHENG Su (郑苏), XIE Xiaomin (谢晓敏)

    Published 2023-07-01
    “…Results A total of 10 articles were selected, including 1 clinical decisions, 2 evidence summary, 5 guidelines, 1expert opinion, 1 expert consensus, best evidences were summarized from the 5 aspects of pain evaluation, intervention, monitoring, education and organization. …”
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    Article
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    Development of quality indicators for hand osteoarthritis care – Results from an European consensus study by Daniel H. Bordvik, Yeliz Prior, Rachael Bamford, Francis Berenbaum, Mathilda Björk, Thalita Blanck, Barbara Slatkowsky Christensen, Krysia Dziedzic, John Edwards, Nazemin Gilanliogullari, Carol Graham, Ida K. Haugen, Margreet Kloppenburg, Hellen Laheij, Marco J.P.F. Ritt, Tanja Stamm, Anne Therese Tveter, Nina Østerås, Ingvild Kjeken

    Published 2025-03-01
    “…Conclusion: The OA-QI v.2 was successfully adapted into a 16-item Hand OA-QI set ensuring alignment with international care standards for hand OA through literature review, international expert panels and patient feedback on language and layout.…”
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    Classification and diagnosis of cervical lesions based on colposcopy images using deep fully convolutional networks: A man-machine comparison cohort study by Binhua Dong, Huifeng Xue, Ye Li, Ping Li, Jiancui Chen, Tao Zhang, Lihua Chen, Diling Pan, Peizhong Liu, Pengming Sun

    Published 2025-01-01
    “…To diagnose the same 870 test images, the Dense-U-Net system took 1.76 ± 0.09 min, while the expert, senior, and junior colposcopists took 716.3 ± 49.76, 892.1 ± 92.30, and 3034.7 ± 259.51 min, respectively. …”
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