目的探究标准化以问题为中心的医学课程(problem-centered medical curriculum,PCMC)联合案例教学法(case-based learning,CBL)在麻醉科本科实习生教学中的应用效果。方法选取2023年7月—2024年6月天津医科大学第二医院麻醉科的100名本...目的探究标准化以问题为中心的医学课程(problem-centered medical curriculum,PCMC)联合案例教学法(case-based learning,CBL)在麻醉科本科实习生教学中的应用效果。方法选取2023年7月—2024年6月天津医科大学第二医院麻醉科的100名本科实习生作为研究对象,根据不同教学方法分为对照组与研究组,各50名。对照组采用CBL教学;研究组采用标准化PCMC联合CBL教学。比较两组的考核成绩、核心能力及教学满意度。结果研究组的理论成绩和操作成绩分别为(94.63±4.27)分与(95.75±3.91)分,均高于对照组的(84.28±5.03)分与(85.62±4.38)分,差异均有统计学意义(t=12.164,15.813;P均<0.05)。研究组的各项核心能力评分均高于对照组,差异均有统计学意义(P均<0.05)。研究组教学满意度高于对照组,差异有统计学意义(P<0.05)。结论标准化PCMC联合CBL教学可提升麻醉科本科实习生的考核成绩、核心能力及教学满意度。展开更多
The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by u...The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by using it.But due to the limited time in a lesson,many English teachers adopt a simple approach to teach grammar,in which students are required to memorize the rules first and then practice a lot.This approach is effec-展开更多
目的针对当前甲状腺结节检出率上升以及超声医生应用甲状腺影像报告和数据系统(Thyroid Imaging Reporting and Data System,TI-RADS)不规范的问题,探索通过案例教学法(Case-based Learning,CBL)与PDCA循环相结合的培训模式提升超声医生...目的针对当前甲状腺结节检出率上升以及超声医生应用甲状腺影像报告和数据系统(Thyroid Imaging Reporting and Data System,TI-RADS)不规范的问题,探索通过案例教学法(Case-based Learning,CBL)与PDCA循环相结合的培训模式提升超声医生对TI-RADS的掌握程度。方法于2023年9月—2023年11月在陕西省人民医院超声科开展培训,内容涵盖TI-RADS理论学习、分层病例阅片、病理知识讲解及临床实践反馈,比较培训前后理论测试、阅片准确率及临床应用变化。结果共有50名超声医生完成培训,对甲状腺结节良恶性判断的准确率得到提升(57.6%vs.63.3%,P=0.003),TI-RADS 4a类分类准确率由38.0%提高至58.0%(P<0.001),而超声报告4类及4a类的占比明显下降,以及超声造影(60.0%vs.82.0%,P<0.001)和细针穿刺活检(80.0%vs.90.0%,P<0.001)结果中恶性占比显著提高。结论CBL与PDCA循环相结合的培训模式可有效提升超声医生对TI-RADS的掌握能力。展开更多
As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
文摘目的探究标准化以问题为中心的医学课程(problem-centered medical curriculum,PCMC)联合案例教学法(case-based learning,CBL)在麻醉科本科实习生教学中的应用效果。方法选取2023年7月—2024年6月天津医科大学第二医院麻醉科的100名本科实习生作为研究对象,根据不同教学方法分为对照组与研究组,各50名。对照组采用CBL教学;研究组采用标准化PCMC联合CBL教学。比较两组的考核成绩、核心能力及教学满意度。结果研究组的理论成绩和操作成绩分别为(94.63±4.27)分与(95.75±3.91)分,均高于对照组的(84.28±5.03)分与(85.62±4.38)分,差异均有统计学意义(t=12.164,15.813;P均<0.05)。研究组的各项核心能力评分均高于对照组,差异均有统计学意义(P均<0.05)。研究组教学满意度高于对照组,差异有统计学意义(P<0.05)。结论标准化PCMC联合CBL教学可提升麻醉科本科实习生的考核成绩、核心能力及教学满意度。
文摘The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by using it.But due to the limited time in a lesson,many English teachers adopt a simple approach to teach grammar,in which students are required to memorize the rules first and then practice a lot.This approach is effec-
文摘目的针对当前甲状腺结节检出率上升以及超声医生应用甲状腺影像报告和数据系统(Thyroid Imaging Reporting and Data System,TI-RADS)不规范的问题,探索通过案例教学法(Case-based Learning,CBL)与PDCA循环相结合的培训模式提升超声医生对TI-RADS的掌握程度。方法于2023年9月—2023年11月在陕西省人民医院超声科开展培训,内容涵盖TI-RADS理论学习、分层病例阅片、病理知识讲解及临床实践反馈,比较培训前后理论测试、阅片准确率及临床应用变化。结果共有50名超声医生完成培训,对甲状腺结节良恶性判断的准确率得到提升(57.6%vs.63.3%,P=0.003),TI-RADS 4a类分类准确率由38.0%提高至58.0%(P<0.001),而超声报告4类及4a类的占比明显下降,以及超声造影(60.0%vs.82.0%,P<0.001)和细针穿刺活检(80.0%vs.90.0%,P<0.001)结果中恶性占比显著提高。结论CBL与PDCA循环相结合的培训模式可有效提升超声医生对TI-RADS的掌握能力。
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
