bluuebunny/crossref_metadata_2025_split huggingface dataset:
SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images
Authors: Wang Shuo , Liu Yuanning , Zhang Hao , Liu Zhen 
Published: March 2024

AbstractCancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer.
Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.)
Authors: Yeon Jeyun , Nguyen Thuy Tien Phan , Kim Minkyung , Sim Sung-Chur 
Published: March 2024

Abstract
Background
Genomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively.

Results
Large phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594–0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780–0.835), fruit width (0.791–0.865), and pericarp thickness (0.643–0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768−12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets.

Conclusion
Our study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.
A mapping review of interventions to address patients who frequently seek care in the emergency department
Authors: Memedovich Ally , Asante Benedicta , Khan Maha , Eze Nkiruka , Holroyd Brian R. , Lang Eddy , Kashuba Sherri , Clement Fiona 
Published: March 2024

Abstract
Background
The high utilization of acute care services, particularly emergency departments (ED), continues to be a significant concern for healthcare providers. Numerous approaches have been studied to meet the care needs of patients who frequently seek care in the ED; however, there is no comprehensive review of the current literature base. As such, a current understanding of the interventions initiated within the ED to address the needs of frequent users is required. This mapping review had three objectives: identify the characteristics associated with the need to frequently seek care in the ED; identify interventions implemented to address the needs of this population; and identify gaps in the current evidence base.

Methods
A knowledge map was created by scoping the literature to identify characteristics associated with frequent ED use and interventions implemented to address frequent use. Then, a literature search was conducted to determine what has been implemented by EDs to reduce frequent ED use. The literature was searched from 2013 to January 2023. MeSH terms and keywords were used to identify relevant studies. Studies implementing an intervention for those with characteristics associated with frequent ED use and reporting on ED use were included.

Results
Twenty-three (23) controlled trials and 35 observational studies were included. The most common populations were older adults, those with chronic conditions, and generic “frequent users”. No studies assessed Indigenous Peoples or racial minorities, and few assessed patients with a disability or patients experiencing homelessness. The most common interventions were referrals, care plans, case management, care coordination, and follow-up phone calls. Most studies reported ED revisits, hospitalization, costs, length-of-stay, or outpatient utilization. Few assessed patient or staff perspectives. About one-third of studies (n = 24) reported significant reductions in ED revisits.

Conclusions
Similar interventions, mainly focused on care coordination and planning, have been implemented to address frequent use of the ED. There are still significant gaps in the populations that have been studied. Efforts now must be undertaken to study more diverse populations whose care needs are not being met elsewhere and thus frequent the ED often.
Evaluating methods for risk prediction of Covid-19 mortality in nursing home residents before and after vaccine availability: a retrospective cohort study
Authors: Aryal Komal , Mowbray Fabrice I. , Miroshnychenko Anna , Strum Ryan P. , Dash Darly , Hillmer Michael P. , Malikov Kamil , Costa Andrew P. , Jones Aaron 
Published: March 2024

Abstract
Background
SARS-CoV-2 vaccines are effective in reducing hospitalization, COVID-19 symptoms, and COVID-19 mortality for nursing home (NH) residents. We sought to compare the accuracy of various machine learning models, examine changes to model performance, and identify resident characteristics that have the strongest associations with 30-day COVID-19 mortality, before and after vaccine availability.

Methods
We conducted a population-based retrospective cohort study analyzing data from all NH facilities across Ontario, Canada. We included all residents diagnosed with SARS-CoV-2 and living in NHs between March 2020 and July 2021. We employed five machine learning algorithms to predict COVID-19 mortality, including logistic regression, LASSO regression, classification and regression trees (CART), random forests, and gradient boosted trees. The discriminative performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for each model using 10-fold cross-validation. Model calibration was determined through evaluation of calibration slopes. Variable importance was calculated by repeatedly and randomly permutating the values of each predictor in the dataset and re-evaluating the model’s performance.

Results
A total of 14,977 NH residents and 20 resident characteristics were included in the model. The cross-validated AUCs were similar across algorithms and ranged from 0.64 to 0.67. Gradient boosted trees and logistic regression had an AUC of 0.67 pre- and post-vaccine availability. CART had the lowest discrimination ability with an AUC of 0.64 pre-vaccine availability, and 0.65 post-vaccine availability. The most influential resident characteristics, irrespective of vaccine availability, included advanced age (≥ 75 years), health instability, functional and cognitive status, sex (male), and polypharmacy.

Conclusions
The predictive accuracy and discrimination exhibited by all five examined machine learning algorithms were similar. Both logistic regression and gradient boosted trees exhibit comparable performance and display slight superiority over other machine learning algorithms. We observed consistent model performance both before and after vaccine availability. The influence of resident characteristics on COVID-19 mortality remained consistent across time periods, suggesting that changes to pre-vaccination screening practices for high-risk individuals are effective in the post-vaccination era.
A database for oncological research and quality assurance: implementation and first experiences with the University Clinical Cancer Registry Regensburg
Authors: Saibold Anna , Koller Michael , Mueller Karolina , Koelbl Oliver , Vielsmeier Veronika , Pukrop Tobias , Spies Oliver , Eilers Vivian , Brese Cathleen , Amann Denise , Maurer Julia 
Published: March 2024

AbstractLegal requirements, certification specifications, as well as the demand for real world data on cancer research and treatment led to the decision to establish the University Clinical Cancer Registry Regensburg. The first organizational step in the implementation process of this oncological data registry was the evaluation and acquisition of suitable tumor documentation and database software. For this purpose, an evaluation matrix comprising required database software criteria was designed and consented by a multidisciplinary group of experts. Next, a yearly report of the Institute for Cancer Center Certification (OnkoZert 2019) was considered to identify database software already in use. The identified systems were rated according to the established criteria matrix and other relevant aspects. Onkostar was the system considered most suited for building up an oncological data repository. In the second step, the central IT department implemented Onkostar on-premise and migrated digitally available data after an adaptation and verification process. In parallel, a uniformed process for handling emerging oncological research questions was established. For research requirements, a data analysis concept was established comprising a proposal for data extraction, procedural instructions, and statistical training materials. In the final step, the implemented software and the process for handling research requirements in practice were evaluated by using two exemplary use cases with the focus on clinic-wide analyses and currently relevant scientific topics. A 2-month test phase conducted by various user groups showed a preference for Onkostar tumor documentation software from IT-Choice, mainly because of its adjustability to support research and treatment. Newly added and migrated data can be used for certification and research purposes. This software also provides support in current tumor documentation by displaying the course of cancer disease for individual patients over time. Such oncological data registries can be a powerful tool for legally required cancer registration, the certification of medical centers, as well as for additional oncological research. Tumor databases can be helpful in projects on cancer treatment and scientific aims. The experiences made at the University Hospital Regensburg may be used as a guidance for implementing clinical databases in similar settings with interdisciplinary responsibilities.
Accuracy of urgency allocation in patients with shortness of breath calling out-of-hours primary care: a cross-sectional study
Authors: Spek Michelle , Venekamp Roderick P. , de Groot Esther , Geersing Geert-Jan , Erkelens Daphne C. A. , van Smeden Maarten , Dobbe Anna S. M. , Delissen Mathé , Rutten Frans H. , Zwart Dorien L. 
Published: March 2024

Abstract
Background
In out-of-hours primary care (OHS-PC), semi-automatic decision support tools are often used during telephone triage. In the Netherlands, the Netherlands Triage Standard (NTS) is used. The NTS is mainly expert-based and evidence on the diagnostic accuracy of the NTS’ urgency allocation against clinically relevant outcomes for patients calling with shortness of breath (SOB) is lacking.

Methods
We included data from adults (≥18 years) who contacted two large Dutch OHS-PC centres for SOB between 1 September 2020 and 31 August 2021 and whose follow-up data about final diagnosis could be retrieved from their own general practitioner (GP).
The diagnostic accuracy (sensitivity and specificity with corresponding 95% confidence intervals (CI)) of the NTS’ urgency levels (high (U1/U2) versus low (U3/U4/U5) and ‘final’ urgency levels (including overruling of the urgency by triage nurses or supervising general practitioners (GPs)) was determined with life-threatening events (LTEs) as the reference. LTEs included, amongst others, acute coronary syndrome, pulmonary embolism, acute heart failure and severe pneumonia.

Results
Out of 2012 eligible triage calls, we could include 1833 adults with SOB who called the OHS-PC, mean age 53.3 (SD 21.5) years, 55.5% female, and 16.6% showed to have had a LTE. Most often severe COVID-19 infection (6.0%), acute heart failure (2.6%), severe COPD exacerbation (2.1%) or severe pneumonia (1.9%).
The NTS urgency level had a sensitivity of 0.56 (95% CI 0.50–0.61) and specificity of 0.61 (95% CI 0.58–0.63). Overruling of the NTS’ urgency allocation by triage nurses and/or supervising GPs did not impact sensitivity (0.56 vs. 0.54, p = 0.458) but slightly improved specificity (0.61 vs. 0.65, p < 0.001).

Conclusions
The semi-automatic decision support tool NTS performs poorly with respect to safety (sensitivity) and efficiency (specificity) of urgency allocation in adults calling Dutch OHS-PC with SOB. There is room for improvement of telephone triage in patients calling OHS-PC with SOB.

Trial registration
The Netherlands Trial Register, number: NL9682.
Communication barriers faced by pharmacists when managing patients with hypertension in a primary care team: a qualitative study
Authors: Tan Reuben , Kawaja Ariffin , Ooi Swee Phaik , Ng Chirk Jenn 
Published: March 2024

Abstract
Background
As primary care pharmacists take on an increasingly important and collaborative role in managing patients with chronic diseases, communication barriers with patients and healthcare colleagues have emerged. This study aimed to explore the communication barriers faced by pharmacists when managing patients with hypertension in a primary care team.

Methods
Twelve pharmacists working in five government primary care clinics were interviewed by a researcher using a topic guide. The interviews were audio-recorded, transcribed verbatim and subjected to thematic analysis.

Results
Pharmacists’ management of patients with hypertension was found to be affected by communication challenges at three different levels: between pharmacists and patients, pharmacists and physicians, and physicians and patients. Barriers to communication between pharmacists and patients include language barrier, physical disabilities, medication brand changes, and specific challenges faced during video consultations. Barriers to communication between pharmacists and physicians include lack of access to patient information across institutions on the electronic medical records (EMR), inadequate and inappropriate documentation by physicians, and disruptive and ineffective phone calls by pharmacists to physicians. Barriers to communication between physicians and patients had a spillover effect on pharmacists; these barriers included language barrier, patients not discussing medication nonadherence with physicians, and conflicting advice given by physicians and pharmacists.

Conclusions
The communication barriers pharmacists faced when managing patients with hypertension involved multiple stakeholders. Many of the challenges resulted in patients having difficulty understanding and adhering to their management plan. Effective interventions to foster stronger interprofessional relationships and create a conducive platform of communication should be developed to address these communication barriers.
Comparative efficacy and safety of subcutaneous infliximab and vedolizumab in patients with Crohn’s disease and ulcerative colitis included in randomised controlled trials
Authors: Peyrin‐Biroulet Laurent , Arkkila Perttu , Armuzzi Alessandro , Danese Silvio , Ferrante Marc , Jordi Guardiola  , Jahnsen Jørgen , Louis Edouard , Lukáš Milan , Reinisch Walter , Roblin Xavier , Smith Philip J , Kwon Taek , Kim Jeeyoung , Yoon Sangwook , Kim Dong-Hyeon , Atreya Raja 
Published: March 2024

Abstract
Background
While indirect comparison of infliximab (IFX) and vedolizumab (VDZ) in adults with Crohn’s disease (CD) or ulcerative colitis (UC) shows that IFX has better effectiveness during induction, and comparable efficacy during maintenance treatment, comparative data specific to subcutaneous (SC) IFX (i.e., CT-P13 SC) versus VDZ are limited.

Aim
Pooled analysis of randomised studies to compare efficacy and safety with IFX SC and VDZ in moderate-to-severe inflammatory bowel disease.

Methods
Parallel-group, randomised studies evaluating IFX SC and VDZ in patients with moderate-to-severe CD or UC were identified. Eligible studies reported ≥ 1 prespecified outcome of interest at Week 6 (reflecting treatment during the induction phase) and/or at 1 year (Weeks 50-54; reflecting treatment during the maintenance phase). Prespecified efficacy and safety outcomes considered in this pooled analysis included the proportions of patients achieving disease-specific clinical responses, clinical remission, or discontinuing due to lack of efficacy, and the proportions of patients experiencing adverse events (AEs), serious AEs, infections, serious infections, or discontinuing due to AEs. Data from multiple studies or study arms were extracted and pooled using a random-effect model; comparative analyses were performed separately for patients with CD and UC.

Results
We identified three eligible CD trials and four eligible UC trials that assigned over 1200 participants per disease cohort to either IFX SC or VDZ. In patients with CD, intravenous induction therapy with IFX demonstrated better efficacy (non-overlapping 95% confidence intervals [CIs]) compared with VDZ; during the maintenance phase, IFX SC showed numerically better efficacy (overlapping 95% CIs) than VDZ. A lower proportion of IFX SC-treated patients discontinued therapy due to lack of efficacy over 1 year. In patients with UC, efficacy profiles were similar with IFX SC and VDZ during the induction and maintenance phases, and a lower proportion of IFX SC-treated patients discontinued therapy due to lack of efficacy over 1 year. In both cohorts, safety profiles for IFX SC and VDZ were generally comparable during 1 year.

Conclusion
IFX SC demonstrated better efficacy than VDZ in patients with CD, and similar efficacy to VDZ in patients with UC; 1-year safety was comparable with IFX SC and VDZ.
Validation of a fall rate prediction model for community-dwelling older adults: a combined analysis of three cohorts with 1850 participants
Authors: Wapp Christina , Mittaz Hager Anne-Gabrielle , Rikkonen Toni , Hilfiker Roger , Biver Emmanuel , Ferrari Serge , Kröger Heikki , Zwahlen Marcel , Zysset Philippe 
Published: March 2024

Abstract
Background
Fragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction.

Methods
A combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage.

Results
The coefficient estimates for the prior number of falls were consistent among the three studies. Higgin’s I2 as heterogeneity measure ranged from 0 to 55.39%. The overall coefficient estimates indicated that the expected fall rate increases with an increasing number of previous falls. External model validation revealed that the prediction errors for the data sets were independent of the model to which they were applied.

Conclusion
This analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.