Evidence-based nursing has penetrated into various nursing branches in mainland China and become a well-recognized and relatively mature research domain. More importance should be attached to the study design, methodological, and reporting quality of evidence-based nursing projects.

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Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low-cost test) can potentially solve this problem. In this study, we develop deep learning scores to detect osteoporosis and VF based on lateral spine radiography and investigate whether their use can improve referral of high-risk individuals to bone-density testing. The derivation cohort consisted of patients aged 50 years or older who underwent lateral spine radiography in Severance Hospital, Korea, from January 2007 to December 2018, providing a total of 26,299 lateral spine plain X-rays for 9276 patients (VF prevalence, 18.6%; osteoporosis prevalence, 40.3%). Two individual deep convolutional neural network scores to detect prevalent VF (VERTE-X pVF score) and osteoporosis (VERTE-X osteo score) were tested on an internal test set (20% hold-out set) and external test set (another hospital cohort [Yongin], 395 patients). VERTE-X pVF, osteo scores, and clinical models to detect prevalent VF or osteoporosis were compared in terms of the areas under the receiver-operating-characteristics curves (AUROCs). Net reclassification improvement (NRI) was calculated when using deep-learning scores to supplement clinical indications for classification of high-risk individuals to dual-energy X-ray absorptiometry (DXA) testing. VERTE-X pVF and osteo scores outperformed clinical models in both the internal (AUROC: VF, 0.93 versus 0.78; osteoporosis, 0.85 versus 0.79) and external (VF, 0.92 versus 0.79; osteoporosis, 0.83 versus 0.65; p < 0.01 for all) test sets. VERTE-X pVF and osteo scores improved the reclassification of individuals with osteoporosis to the DXA testing group when applied together with the clinical indications for DXA testing in both the internal (NRI 0.10) and external (NRI 0.14, p < 0.001 for all) test sets. The proposed method could detect prevalent VFs and osteoporosis, and it improved referral of individuals at high risk of fracture to DXA testing more than clinical indications alone.

Along with the development of the data‐driven research paradigm, there are exponentially increasing datasets, which bring challenges to researchers in the efficient retrieval of relevant datasets. Previous studies mainly focused on query expansion methods based on sparse retrieval models to improve the accuracy and recall in retrieval. We investigated the use of semantically rich information to retrieve relevant datasets and the benefits of using domain‐specific dense vector representation as opposed to general representation. First, we used pairs of metadata fields that have semantic relevance to construct the domain‐specific weakly supervised training data. Then, a pre‐trained transformer‐based deep learning model is fine‐tuned on the training data using the contrastive learning method. Finally, dense vector representations of the queries and datasets are obtained based on the fine‐tuned model. The relevance of a dataset to a query is measured by the similarity between the vectors. To evaluate the performance of the proposed model, we collected 104,683 datasets from 13 research data repositories, recruited volunteers to design research‐oriented queries, and annotated the retrieval results. The experimental results show that compared with the domain‐independent fine‐tuned model, our proposed method can improve the NDCG@10 score by about 5%.

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