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1.
目的 探索Kolb学习模型在护理实习生人文关怀能力培养中的应用效果。方法 以2019年6月和2020年6月进入昆山市中医医院临床实习的护生作为研究对象,其中2019年实习的护生109名为对照组,2020年实习的护生116名为观察组。观察组采取基于Kolb学习模型的人文关怀临床带教,对照组采取传统方法行人文关怀临床带教。实习结束时分别采用护生人文关怀量表和自主学习能力量表来测评两组护生的学习效果。结果 观察组护生人文关怀能力和自主学习能力各维度得分均高于对照组,差异具有统计学意义(P<0.01,P<0.05)。结论 Kolb学习模型应用于护生人文关怀教学中,可以提高护生人文关怀能力及自主学习能力,提升教学质量。  相似文献   
2.
We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneity-aware deep Multiple Instance LEarning (DeepSMILE). We apply DeepSMILE to the task of Homologous recombination deficiency (HRD) and microsatellite instability (MSI) prediction. We utilize contrastive self-supervised learning to pre-train a feature extractor on histopathology tiles of cancer tissue. Additionally, we use variability-aware deep multiple instance learning to learn the tile feature aggregation function while modeling tumor heterogeneity. For MSI prediction in a tumor-annotated and color normalized subset of TCGA-CRC (n=360 patients), contrastive self-supervised learning improves the tile supervision baseline from 0.77 to 0.87 AUROC, on par with our proposed DeepSMILE method. On TCGA-BC (n=1041 patients) without any manual annotations, DeepSMILE improves HRD classification performance from 0.77 to 0.81 AUROC compared to tile supervision with either a self-supervised or ImageNet pre-trained feature extractor. Our proposed methods reach the baseline performance using only 40% of the labeled data on both datasets. These improvements suggest we can use standard self-supervised learning techniques combined with multiple instance learning in the histopathology domain to improve genomic label classification performance with fewer labeled data.  相似文献   
3.
AimThe purpose of this integrative review is to provide a comprehensive review of ethical considerations for host communities and nursing programs in planning, implementing and evaluating global health experiences for nursing students.BackgroundGlobal health experiences for nursing students are proliferating rapidly across university settings while at the same time decreasing the average time spent in the host country engaged with local communities. Global health experiences are an area where students can experience ethics as it is applied across varied contexts including resource limited international settings. As nursing education expands its global programming, exploring the ethical implications of designing, implementing and evaluating GHEs becomes pivotal to build respectful, sustainable relationships with global partners and best prepare nursing students for ethical professional practice in an interconnected world.DesignWe conducted an integrative review to examine ethical considerations in development of ethical global health experiences that benefit, rather than harm, host communities and participating nursing student guests.MethodsThe search included articles published in English language, peer-reviewed journals between 1998 and 2021 that discussed ethics in the context of nursing students traveling internationally for global health experiences. Eighteen articles met criteria for review.ResultsOverall, findings demonstrate relatively little research specific to ethical engagement in global health experiences. The articles in this integrative review discussed a range of ethical attributes including reciprocity or collaboration, respect, sustainability or commitment, justice and openness. Recommendations based on research and non-research articles are provided.ConclusionsEthical comportment in global health experiences requires careful planning, implementation and evaluation to assure an equitable and sustainable partnership between host community, faculty and nursing student guests.  相似文献   
4.
BackgroundDissection of lymph nodes at the roots of the inferior mesenteric artery (IMAN) should be offered only to selected patients at a major risk of developing IMAN involvement. The aim of this study is to present the first artificial intelligence (AI) models to predict IMAN metastasis risk in the left colon and rectal cancer patients.MethodsA total of 2891 patients with descending colon including splenic flexure, sigmoid colon and rectal cancer undergoing major primary surgery and IMAN dissection were included as a study cohort, which was then split into a training set (67%) and a testing set (33%). Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression model. Seven AI algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Decision Tree Classifier (DTC), Random Forest (RF) classifier, and Multilayer Perceptron (MLP), as well as traditional multivariate LR model were employed to construct predictive models. The optimal hyperparameters were determined with 5 fold cross-validation. The predictive performance of models and the expert surgeon was assessed and compared in the testing set independently.ResultsThe IMAN involvement incidence was 4.6%. The optimal set of features selected by LASSO included 10 characteristics: neoadjuvant treatment, age, synchronous liver metastasis, synchronous lung metastasis, signet ring adenocarcinoma, neural invasion, lymphovascular invasion, CA199, endoscopic obstruction, T stage evaluated by MRI. The most accurate model derived from MLP showed excellent prediction power with area under the receiver operating characteristic curve (AUROC) of 0.873 and produced 81.0% recognition sensitivity and 82.5% specificity in the testing set independently. In contrast, the judgment of IMAN metastasis by expert surgeon yield rather imprecise and unreliable results with a significantly lower AUROC of 0.509. Additionally, the proposed MLP had the highest net benefits and the largest reduction of unnecessary IMAN dissection without the cost of additional involved IMAN missed.ConclusionMLP model was able to maintain its prediction accuracy in the testing set better than other models and expert surgeons. Our MLP model could be used to help identify IMA nodal metastasis and to select candidates for individual IMAN dissection.  相似文献   
5.
《Genetics in medicine》2022,24(11):2329-2337
PurposeThe variable expressivity and multisystem features of Noonan syndrome (NS) make it difficult for patients to obtain a timely diagnosis. Genetic testing can confirm a diagnosis, but underdiagnosis is prevalent owing to a lack of recognition and referral for testing. Our study investigated the utility of using electronic health records (EHRs) to identify patients at high risk of NS.MethodsUsing diagnosis texts extracted from Cincinnati Children’s Hospital’s EHR database, we constructed deep learning models from 162 NS cases and 16,200 putative controls. Performance was evaluated on 2 independent test sets, one containing patients with NS who were previously diagnosed and the other containing patients with undiagnosed NS.ResultsOur novel method performed significantly better than the previous method, with the convolutional neural network model achieving the highest area under the precision-recall curve in both test sets (diagnosed: 0.43, undiagnosed: 0.16).ConclusionThe results suggested the validity of using text-based deep learning methods to analyze EHR and showed the value of this approach as a potential tool to identify patients with features of rare diseases. Given the paucity of medical geneticists, this has the potential to reduce disease underdiagnosis by prioritizing patients who will benefit most from a genetics referral.  相似文献   
6.
目的评价沉浸式基于问题的学习(PBL)模式在血液净化血管通路教学中的效果。 方法研究对象为2019年9月至2021年9月贵州省各地医院参加血液净化血管通路培训的各级专科医生,前4期学员采取传统教学模式为对照组,后4期学员采用沉浸式PBL教学模式为研究组。培训结束后从理论知识、技能、满意度、评判性思维能力和带教老师自评方面进行考核。 结果与对照组相比,研究组的理论知识分数、临床操作技能分数,对培训的形式、规模、带教老师知识储备、培训效果的满意度,以及在寻求真相、求知欲、认知成熟度、系统化能力、分析能力及评判性思维能力方面的总分均增高(P<0.05)。此外,研究组的教师在培训场景、适时提问和讨论、操作规范性、有效考核评估以及突击应变能力方面的自评分数亦显著高于对照组(P<0.05)。 结论沉浸式PBL教学模式可以使血液净化血管通路培训的学员及带教老师在专业知识及实践操作能力方面得到进一步提升,值得推广。  相似文献   
7.
8.
目的 探讨基于深度学习的CT血流储备分数(FFRCT)在可疑冠心病病人中应用的可行性,分析缺血性病变(FFRCT≤0.80)的预测因素及对治疗决策的影响。方法 回顾性纳入因疑似冠心病行冠状动脉CT血管成像(CCTA)的病人292例,其中男187例,女105例,平均年龄(65.8±10.3)岁。利用CCTA影像将狭窄程度分为轻度 (≥25%且<50%)、中度(≥50%且<70%)和重度(≥70%且<99%)。采用基于深度学习的FFRCT软件对病人的CCTA数据进行测量。根据FFRCT数值范围将病人分为阳性组(FFRCT≤0.80,102例)和阴性组(FFRCT>0.80,190例)。2组病人的一般资料、CCTA上的血管特征及血运重建,以及基于FFRCT与CCTA制定的治疗策略的比较采用Mann-Whitney U 检验、t检验及卡方检验。采用Logistic回归分析FFRCT≤0.80的独立预测因素。结果 阳性组病人的年龄更大,男性更多,高血压、糖尿病和吸烟的比例均高于阴性组(均P<0.05)。阳性组较阴性组病人更多的表现为中重度狭窄(分别为80.4%和28.4%),更多的病人行血运重建术(分别为56.8%和11.1%),均P<0.05。74例病人(25.3%)基于FFRCT的结果治疗决策发生改变。多因素Logsitic回归分析显示,高血压(OR=2.245)、糖尿病(OR=2.238)及中重度狭窄(OR=8.837)是FFRCT≤0.80的独立预测因素(均P<0.05)。结论 基于深度学习的FFRCT技术在可疑冠心病病人中的应用是可行的,高血压、糖尿病及中重度狭窄是FFRCT≤0.80的独立预测因素,FFRCT可能影响病人的治疗决策。  相似文献   
9.
《Radiography》2022,28(3):793-797
IntroductionChanging working practices, student numbers, workforce demands, and deficits, have created a need to consider new ways of radiography student training. One suggestion could be to implement Peer Assisted Learning (PAL) during clinical placements. PAL utilises social constructivist theories, where peer tutors teach lower or same level tutees, reinforcing and practicing material formally taught. The aim of this study was to trial an intervention of PAL, co-designed between the university and students and evaluated to identify opportunities and challenges.MethodsUsing participatory action research 8 final year student volunteers trialled a 3-week intervention, where they delivered PAL to first years, tutoring on first year radiographic clinical practice. Focus groups were held pre and post intervention to gather qualitative data.ResultsFocus group discussions were transcribed and collectively thematically analysed. Two students and the primary researcher took part in the analysis.ConclusionStudents identified benefits and challenges to PAL. Issue around preparing for and being a peer tutor are also discussed.Further study involving experiences of first year students and clinical colleagues is required.Implications for practicePeer-tutoring has potential benefits to students to facilitate the development of skills related to image analysis and critique as well as radiographic anatomy and patient positioning.  相似文献   
10.
Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.  相似文献   
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