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Purpose: This study investigated editors’ and researcher’s experiences with preprints and their attitudes towards preprint policies in Korea.Methods: From December 30, 2019 to January 10, 2020, a Google Forms survey was mailed to members of the Korean Council of Science Editors and the Korean Federation of Science and Technology Societies. The 16 survey items included two demographic items, six items on experience with preprints, five 5-point Likert-scale items on attitudes towards preprints, and three items on advantages and disadvantages.Results: Out of 365 respondents, 56 had deposited their manuscripts on preprint servers, while 49 stated that they allowed preprints in their journals. More than half of the respondents expressed favorable attitudes towards prioritizing preprint deposition, promotion of open access, rapid feedback on preprints, earlier citations, and evidence of research work. Responders in engineering had more experience with the concept of preprints, and were more likely to have heard about preprint servers and preprint deposition by other researchers, than those in medicine. Half of the editors disagreed with the need for preprints, for reasons including a lack of scientific integrity, stealing ideas/scooping data, priority issues regarding research ideas, and copyright problems.Conclusion: The above results showed that preprints are still not actively used in Korea. Although experiences with preprints were not widespread, more than half of the respondents showed favorable attitudes towards preprints. More of a consensus should emerge for preprint policies to be accepted by editors in Korea.

Many object re-identification (Re-ID) methods that depend on large-scale training datasets have been proposed in recent years. However, the performance of these methods degrades dramatically when insufficient training data are available. To address this challenging problem, we propose a few-shot object re-identification (FSOR) method that enhances the generalization and discrimination abilities of object Re-ID models trained on small datasets. This method applies two novel techniques: reparameterization for feature vectors and dual-distance metric learning. The reparameterization mechanism transforms the primary feature vector of each input image into a Gaussian distribution to enhance the robustness of the FSOR method when performing object Re-ID tasks. The dual-distance metric learning technique, called H&C learning, considers both the hard mining distance and the center-point distance between each query sample and each support set of different object identities. H&C learning extracts the characteristics of the entire training dataset more precisely than other approaches and thus improves the discriminative abilities of object Re-ID models. Extensive experiments on both person and vehicle Re-ID datasets, such as Market-1501, DukeMTMC-ReID, CUHK03, and VeRi-776, show that the FSOR method has improved performance and outperforms state-of-the-art methods when the amount of labeled training data is small.

Background Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This approach however is computationally expensive, especially on large-scale datasets. Methods We propose an inference-based method, called RR-BART, which leverages the likelihood-based Bayesian machine learning technique, Bayesian additive regression trees, and uses Rubin’s rule to combine the estimates and variances of the variable importance measures on multiply imputed datasets for variable selection in the presence of MAR data. We conduct a representative simulation study to investigate the practical operating characteristics of RR-BART, and compare it with the bootstrap imputation based methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome among middle-aged women using data from the Study of Women’s Health Across the Nation (SWAN). Results The simulation study suggests that even in complex conditions of nonlinearity and nonadditivity with a large percentage of missingness, RR-BART can reasonably recover both prediction and variable selection performances, achievable on the fully observed data. RR-BART provides the best performance that the bootstrap imputation based methods can achieve with the optimal selection threshold value. In addition, RR-BART demonstrates a substantially stronger ability of detecting discrete predictors. Furthermore, RR-BART offers substantial computational savings. When implemented on the SWAN data, RR-BART adds to the literature by selecting a set of predictors that had been less commonly identified as risk factors but had substantial biological justifications. Conclusion The proposed variable selection method for MAR data, RR-BART, offers both computational efficiency and good operating characteristics and is utilitarian in large-scale healthcare database studies.

This article examines Cold War icons transfigured in new media ecology by looking at the South Korean webtoon (web cartoon) Secretly, Greatly. In featuring the superfluous existence of kanch’ŏp (North Korean spies) dispatched to the South, the graphic narrative interweaves the legacy of national division with the sensibilities of the millennial generation. Feeling that their lives are rendered ingyŏ (surplus) under neoliberal governance, the precarious youth empathetically relate to the history-laden image of spies, whose belonging in society has been disavowed or forsaken. Appropriating earlier aesthetic conventions, Secretly, Greatly thus offers content and a channel through which the surplus generation can express and share their sense of misplacement in the present. In addressing such remediation of inherited memories, this study critically attends to the webtoon’s formal qualities that evoke affective engagement and connective practices in the digital space. By fostering multisensory interactions with the kanch’ŏp-ingyŏ personas on the screen, the graphic power of comics invites the viewer to participate in the characters’ boundary-crossing movements against the geopolitical backdrop of divided Korea. Nonetheless, the webtoon ultimately leaves the survival of North Korean others beyond the realm of the visible because their ordinary coexistence in the South is yet to be imagined.

Objective To derive and validate a comorbidity-based delirium risk index (DRI) to predict postoperative delirium. Data source/study setting Data of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20-25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation. Study design Thirty-six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson-Deyo Index for goodness-of-fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness-of-fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries. Data collection Hip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD-9 codes. Postoperative delirium was defined by using ICD-9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium. Principal findings The derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson-Deyo Comorbidity Index with better goodness-of-fit and predictive performance. Conclusions Delirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.

OBJECTIVE Identifying adverse outcomes and examining trends and causes of nonelective admissions among persons with epilepsy would be beneficial to optimize patient care and reduce health services utilization. We examined the association of epilepsy with discharge status, in-hospital mortality, length-of-stay, and charges. We also examined 10-year trends and causes of hospital admissions among those with and without epilepsy. METHODS Nonelective hospital admission in persons with epilepsy was identified in the 2005-2014 National Inpatient Sample (NIS) using a validated International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) case definition. The NIS is the largest US all-payer database including patient and hospital-level variables, and represents hospitalizations in the general population. Descriptive statistics on trends and causes of admissions and multivariable regression analysis summarizing the association of epilepsy with the outcomes of interest are presented. RESULTS Of 4 718 178 nonelective admissions in 2014, 3.80% (n = 179 461) were in persons with epilepsy. Admissions in persons with epilepsy increased from 14 636 to 179 461 (P

Biological subtypes have been strongly correlated with loco-regional recurrence after definitive treatment for localized breast cancer. Brain metastases (BM) occur in 15% of patient diagnosed with breast cancer (BC). Therefore, we sought to determine whether biologic subtype is predictive of local control (LC) in BC patients with BM treated with stereotactic radiosurgery (SRS). This is a single-center retrospective study conducted with Institutional Review Board approval for the period January 1, 2004–December 31, 2018. Eligibility criteria included (1) histopathologic proven BC with documented biologic subtype, (2) presence of BM treated with SRS (single fraction), (3) follow-up MRIs, and (4) clinical follow-up. Patients were subdivided into three biologic subtypes: ER+/HER2- (“luminal”), HER2+, and ER-/PR-/HER2 (TN). All patients were treated with LINAC-based SRS with dose according to published guidelines. Cox proportional hazard model and Kaplan-Meier were used for statistical analysis of LC and overall survival (OS). With a median follow-up of 11.1 months, 17 BC BM in 50 consecutive patients were included in this study. The median disease-specific GPA was 2.0, and all patients received systemic chemotherapy and/or hormonal therapy. The 12-month LC rates for the entire cohort were 85%, 87%, and 49% for luminal, HER2+, and TN, respectively, with a significantly shorter time to local failure for the TN subtype (p = 0.014). The 12-month OS rates were 83%, 88%, and 80% for luminal, HER2+, and TN, respectively, with a trend toward shorter OS in the TN group. This study shows that in BC patients with BM treated with SRS, biologic subtype affects LC. Consideration of radiation treatment intensification or altered fractionation to improve LC may be indicated for the TN subtype. Further multicenter studies are necessary to corroborate our results.

Background Currently, there are no recognized or validated biomarkers to identify hepatocellular carcinoma patients (HCC) likely to benefit from anti–PD‐1 therapy. We evaluated the relationship between neutrophil‐lymphocyte ratio (NLR) and platelet‐lymphocyte ratio (PLR) and survival outcomes, pretreatment and after three doses (posttreatment) of nivolumab in HCC patients. Methods Medical records of HCC patients treated with nivolumab between June 2016 and July 2018 were reviewed. Kaplan‐Meier analysis and the log‐rank test were used to calculate and compare overall survival between NLR, In this study evaluating 103 patients with advanced hepatocellular carcinoma, lower neutrophil‐lymphocyte ratio (NLR) and platelet‐lymphocyte ratio (PLR) were both strongly predictive of improved survival after three cycles of nivolumab. Baseline NLR was not associated with survival. An elevated NLR and PLR in the posttreatment setting may be an early sign of treatment failure and may warrant consideration for early change in therapy.

We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378) cohorts, and measured TNFR1, TIM3, IL6, ST2, and REG3α via enzyme-linked immunosorbent assay. Performances of the 4 strongest algorithms from the training cohort (TNFR1 + TIM3, TNFR1 + ST2, TNFR1 + REG3α, and ST2 + REG3α) were evaluated in the validation cohort. The algorithm that included only biomarkers of systemic inflammation (TNFR1 + TIM3) had a significantly smaller area under the curve (AUC; 0.57) than the AUCs of algorithms that contained ≥1 GI damage biomarker (TNFR1 + ST2, 0.70; TNFR1 + REG3α, 0.73; ST2 + REG3α, 0.79; all P < .001). All 4 algorithms were able to predict short-term outcomes such as response to systemic corticosteroids and severe GVHD, but the inclusion of a GI damage biomarker was needed to predict long-term outcomes such as 6-month NRM and survival. The algorithm that included 2 GI damage biomarkers was the most accurate of the 4 algorithms for all endpoints.

Additional file 1 Web-based supplementary materials. Supplementary Section 1. Random Forest based Imputation Algorithm - missForest. Supplementary Section 2. Simulation Setup. Supplementary Section 3. Additional tables (Supplementary Table 1–12) and figures (Supplementary Figure 1–2) for simulation study and case study.

The National Lung Screening Trial (NLST) found that low-dose computed tomography (LDCT) screening provided lung cancer (LC) mortality benefit compared to chest radiography (CXR). Considerable research concerns identifying the differential treatment effects that may exist in certain subpopulations. We shed light on several important issues in existing research and highlight the need for further investigation of the heterogeneous comparative effect of LDCT versus CXR, using more flexible and rigorous statistical approaches. We used a high-performance Bayesian machine learning approach designed for censored survival data, accelerated failure time Bayesian additive regression trees model (AFT-BART), to flexibly capture the relationships between the failure time and predictors. We then used the counterfactual framework to draw Markov chain Monte Carlo samples of the individual treatment effect for each participant. Using these posterior samples, we explored the possible treatment effect heterogeneity via a stepwise binary tree approach. When re-analyzed with AFT-BART, LDCT did not have a statistically significant LC or overall mortality benefit compared to CXR. The Asian and Black (particularly those with pack-year ≥ 37 years and without emphysema) NLST population were shown to have enhanced overall mortality benefit from LDCT than the population average. Although inconclusive for LC mortality benefit, Asians, Blacks and Whites with history of chronic obstructive pulmonary disease showed a small trend towards benefit from LDCT. Causal inference with flexible machine learning modeling can provide valuable knowledge for informing treatment decision and planning targeted clinical trials emphasizing personalized medicine approaches.

AIMS: The aim of this study was to improve medication reconciliation and reduce the occurrence of duplicate prescriptions by pharmacists and physicians within 72 hours of hospital admission using an intelligent prescription system combined with the National Health Insurance PharmaCloud system to integrate the database with the medical institution computerized physician order entry (CPOE) system. METHODS: This 2‐year intervention study was implemented in the geriatric ward of a hospital in Taiwan. We developed an integrated CPOE system linked with the PharmaCloud database and established an electronic platform for coordinated communication with all healthcare professionals. Patients provided written informed consent to access their PharmaCloud records. We compared the intervention effectiveness within 72 hours of admission for improvement in pharmacist medication reconciliation, increased at‐home medications documentation and decreased costs from duplicated at‐home prescriptions. RESULTS: The medication reconciliation rate within 72 hours of admission increased from 44.0% preintervention to 86.8% postintervention (relative risk = 1.97, 95% confidence interval [CI]: 1.69–2.31; P

Purpose It aimed to investigate how many retracted articles indexed in KoreaMed were cited in both the Scopus and the Korea Medical Citation Index (KoMCI) databases and to investigate whether the frequency of post-retraction citations was different according to the presence of a retraction mark. Methods Retracted articles from the KoreaMed database were collected on January 28, 2016. Scopus and KoMCI were searched for post-retraction citations, which were defined as citations 1 year after the retraction, excluding retraction-related citations. Results The 114 retracted articles were found in KoreaMed. The proportion of retracted articles in KoreaMed, the Korean medical journal database, through January 2016 was 0.04% (114/256,000). On the journal homepage, a retraction mark was present for 49 of the 114 retracted articles. Of the 114 retracted articles, 45 were cited in Scopus 176 times. Of the 176 citations, 109 (of 36 retracted articles) were post-retraction citations. The number of citations in KoMCI, except for citations of retraction notices, was 33 (of 14 retracted articles). Of those citations, the number of post-retraction citations in KoMCI was 14 (of 8 retracted articles). The presence of a retraction mark did not influence post-retraction citations (P>0.05). Post-retraction citations were frequent in the range of 1 to 3 years. Conclusion Post-retraction citations that were found in both Scopus and the KoMCI occurred frequently for retracted articles in KoreaMed. Adoption of Crossmark is recommended as one choice to prevent post-retraction citations.