Many transactions in web applications are constructed ad hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical ...code fragments. We refer to database operations coordinated by application code as ad hoc transactions. Until now, little is known about them. This paper presents the first comprehensive study on ad hoc transactions. By studying 91 ad hoc transactions among eight popular open-source web applications, we found that (i) every studied application uses ad hoc transactions (up to 16 per application), 71 of which play critical roles; (ii) compared with database transactions, concurrency control of ad hoc transactions is much more flexible; (iii) ad hoc transactions are error-prone—53 of them have correctness issues, and 33 of them are confirmed by developers; and (iv) ad hoc transactions have the potential for improving performance in contentious workloads by utilizing application semantics such as access patterns. Based on these findings, we discuss the implications of ad hoc transactions to the database research community.
Ceiling and floor effects are often observed in social and behavioral science. The current study examines ceiling/floor effects in the context of the
t-
test and ANOVA, two frequently used ...statistical methods in experimental studies. Our literature review indicated that most researchers treated ceiling or floor data as if these data were true values, and that some researchers used statistical methods such as discarding ceiling or floor data in conducting the
t
-test and ANOVA. The current study evaluates the performance of these conventional methods for
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-test and ANOVA with ceiling or floor data. Our evaluation also includes censored regression with regard to its capacity for handling ceiling/floor data. Furthermore, we propose an easy-to-use method that handles ceiling or floor data in
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-tests and ANOVA by using properties of truncated normal distributions. Simulation studies were conducted to compare the performance of the methods in handling ceiling or floor data for
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-test and ANOVA. Overall, the proposed method showed greater accuracy in effect size estimation and better-controlled Type I error rates over other evaluated methods. We developed an easy-to-use software package and web applications to help researchers implement the proposed method. Recommendations and future directions are discussed.
Background Adaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment ...Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers' ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap. Methods Between January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs. Results We report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap. Conclusions REDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization. Trial registration The SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021. Keywords: Research Electronic Data capture (REDCap), Randomized controlled trials (RCT), Adaptive interventions, Sequential multiple assignment Randomized Trial (SMART), Randomization, Experimental design, Reducing human errors, Automation
Cancer‐associated thrombosis (CAT) is a major cause of morbidity and mortality in patients with cancer. Over the past 2 decades, enormous advances have been made in the management of CAT. The growing ...evidence base informing practice has led to the publication of a number of guidelines and guidance documents on the diagnosis and treatment of CAT. The goal of this review is to examine the latest versions of evidence‐based guidelines, highlighting the differences and similarities in their methodology, their disease‐specific content, and recommendations for management. Our analysis shows that for most clinical topics, the different guidelines provide roughly similar management advice. However, there are a number of important clinical topics in CAT that are not currently covered by the existing guidelines. We think inclusion of these topics in future versions of the guidelines will facilitate ongoing efforts to optimize the care of patients with CAT.
Implications for Practice
Cancer‐associated thrombosis (CAT) is a common complication in patients with cancer. This review examines the differences and similarities of the current CAT guidelines methods and recommendations. Current guidelines largely agree on many aspects of CAT management. However, there are a number of topics in CAT that are not currently included in guidelines where evidence‐based guidance would be very helpful for clinicians. Coverage of these topics in future guidelines is encouraged to optimize clinical practice.
Numerous guidelines for cancer‐associated thromboembolism have been published. This review compares recommendations from the most recent cancer‐specific guidelines, identifying areas in which guidance is lacking.
Evidence-based treatments for major depressive disorder (MDD) are not very successful in improving functional and health outcomes. Attention has increasingly been focused on the prevention of MDD.
To ...evaluate the effectiveness of a web-based guided self-help intervention for the prevention of MDD.
Two-group randomized clinical trial conducted between March 1, 2013, and March 4, 2015. Participants were recruited in Germany from the general population via a large statutory health insurance company (ie, insurance funded by joint employer-employee contributions). Participants included 406 self-selected adults with subthreshold depression (Centre for Epidemiologic Studies Depression Scale score ≥16, no current MDD according to Diagnostic and Statistical Manual of Mental Disorders Fourth Edition, Text Revision criteria).
All participants had unrestricted access to usual care (visits to the primary care clinician) and were randomized to either a web-based guided self-help intervention (cognitive-behavioral and problem-solving therapy supported by an online trainer; n = 202) or a web-based psychoeducation program (n = 204).
The primary outcome was time to onset of MDD in the intervention group relative to the control group over a 12-month follow-up period as assessed by blinded diagnostic raters using the telephone-administered Structured Clinical Interview for DSM-IV Axis Disorders at 6- and 12-month follow-up, covering the period to the previous assessment.
Among 406 randomized patients (mean age, 45 years; 73.9% women), 335 (82%) completed the telephone follow-up at 12 months. Fifty-five participants (27%) in the intervention group experienced MDD compared with 84 participants (41%) in the control group. Cox regression analyses controlling for baseline depressive symptom severity revealed a hazard ratio of 0.59 (95% CI, 0.42-0.82; P = .002) at 12-month follow-up. The number needed to treat to avoid 1 new case of MDD was 5.9 (95% CI, 3.9-14.6).
Among patients with subthreshold depression, the use of a web-based guided self-help intervention compared with enhanced usual care reduced the incidence of MDD over 12 months. Further research is needed to understand whether the effects are generalizable to both first onset of depression and depression recurrence as well as efficacy without the use of an online trainer.
German Clinical Trial Registry Identifier: DRKS00004709.
Flux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA ...generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works.
To meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization. Escher-FBA allows users to set flux bounds, knock out reactions, change objective functions, upload metabolic models, and generate high-quality figures without downloading software or writing code. We provide detailed instructions on how to use Escher-FBA to replicate several FBA simulations that generate real scientific hypotheses.
We designed Escher-FBA to be as intuitive as possible so that users can quickly and easily understand the core concepts of FBA. The web application can be accessed at https://sbrg.github.io/escher-fba .
Medical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current ...medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated; hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit.
We present Graph4Med, a web application that relies on a graph database obtained by transforming a relational database. Graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients' health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients' cohort analysis. This way our tool (1) quickly displays the overview of patients' cohort information such as distributions of gender, age, mutations (fusions), diagnosis; (2) provides mutation (fusion) based similarity search and display in a maneuverable graph; (3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients.
We demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization of complex medical data. It is especially useful for patients similarity search based on mutations (fusions), of which vast amounts of data have been generated by NGS in recent years. It can discover relationships and patterns in patients cohorts that are normally hard to grasp. Expanding Graph4Med to more medical databases will bring novel insights into diagnostic and research.
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