Pre-screening Process Research Articles

Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies

Biomarkers for discrimination among different subgroups of idiopathic inflammatory myopathies (IIM) are difficult to identify and may involve multiple laboratory tests and time-consuming procedures. We assessed the potential for artificial intelligence (AI) to extract features such as density of endomysial microvessels based on automatic analysis of the CD31+ vascular network on muscle biopsy images. We also assessed the potential of this technique to save time and its agreement rate with analyses based on the manual selection of microvessels from the same images. A total of 84 images from 84 patients with IIM, diagnosed between 2014 and 2020, were retrieved and analyzed using the Fast Random Forest (FRF) technique. We built a lightweight and explainable algorithm for calculating the pixel percentage of CD31+ endomysial capillaries. The FRF technique applied on images of CD31-stained muscle sections achieved a good performance in the recognition of microvessels by estimating their density over a standard area corresponding to a sample of microscope image. The time spent for this analysis was 90% less than the manual choice of microvessels (estimated time considering the computational time and the time spent to manually detecting the microvessels features). The good performance of the FRF demonstrates that the CD31 pixel percentage of endomysial capillaries is sufficient for a correct estimation. Finally, the paper proposes a procedure to integrate AI in the pre-screening process.

AI-HR: An Approach to Improve Performance of Large Language Model in the Pre-screening Process

This paper introduces the development of an HR-assisting website designed to streamline the pre-screening process using large language models. We improved the GPT APIs performance through Chain-of-Thought prompting and tailored evaluation rubrics to provide more detailed and accurate assessments of job candidates. We also conducted some experiments to investigate some ethical concerns, such as bias in the recruitment process. Based on the test results, we tried to formulate a strategic approach using GPT-4o mini to enhance fairness and inclusivity in the evaluation process.

Conservation-led palaeolimnology: a review of applied palaeolimnology and lessons to improve accessibility and value to conservation practice

AbstractThe integration of palaeolimnology into conservation practice is crucial for effective ecosystem management and restoration. Palaeoecological data provide a unique long-term perspective on key ecological challenges and enable decision makers to better understand pre-disturbance conditions, natural system dynamism and responses to change. Despite this there exist well-recognised accessibility issues and a clear research-implementation gap, in particular, poor communication and lack of understanding of conservation practitioners’ constraints. This study evaluates the accessibility and value of palaeolimnological research to conservation practice by interrogating 60 key applied research papers identified via a rigorous pre-screening process to ensure relevance. The papers were assessed on the use of best practice accessibility criteria, from knowledge-exchange literature, and conservation-practitioner feedback was gathered on the value of conservation recommendations made within the papers. Despite widespread recognition of the importance of accessible research, our review reveals that essential accessibility criteria are inconsistently applied. Although there has been an increase in accessibility practices over time, co-production practices (including co-authorship, co-design of research, and linkage to relevant environmental legislation), showed no significant increase, despite being advocated for by the research community. Practitioner review highlighted the need for research to provide clear, actionable recommendations, and papers that detailed specific management or restoration guidance were particularly well-received, as were those that considered financial implications and summarised their findings more clearly. Equally, many papers were criticised for overly technical language and poor expression, generic suggestions, and a lack of practical consideration in their recommendations. The study highlights the importance of improving accessibility and co-production of knowledge to ensure that research outputs are accessible, relevant, and feasible to guide conservation efforts. To enhance the relevance and impact of applied palaeolimnology, we propose five key recommendations: (1) situate recommendations within existing practice and knowledge; (2) consider the practicalities for practitioners, including material and socio-economic aspects; (3) use clear and simple language; (4) employ easy-to-interpret diagrams and bullet points for recommendations; and (5) improve accessibility of older work. These recommendations are key in helping palaeolimnology progress towards a ‘gold standard’ of applied research, where it can be employed to greater effect to support conservation practice.

Prevalence of FGFR2b Protein Overexpression in Advanced Gastric Cancers During Prescreening for the Phase III FORTITUDE-101 Trial.

Fibroblast growth factor receptor 2 isoform IIIb (FGFR2b) protein overexpression is an emerging biomarker in gastric cancer and gastroesophageal junction cancer (GC). We assessed FGFR2b protein overexpression prevalence in nearly 3,800 tumor samples as part of the prescreening process for a global phase III study in patients with newly diagnosed advanced or metastatic GC. As of June 28, 2024, 3,782 tumor samples from prescreened patients from 37 countries for the phase III FORTITUDE-101 trial (ClinicalTrials.gov identifier: NCT05052801) were centrally tested for FGFR2b protein overexpression by immunohistochemistry (IHC) and had evaluable results. FGFR2b positivity was defined as both any % tumor cells (TC) and ≥10% TC exhibiting moderate-to-strong (2+/3+) membranous FGFR2b staining. Prevalence was analyzed across patient and sample characteristics. FGFR2b protein overexpression at any % and ≥10%, 2+/3+ TC positivity was 37.8% (1,428/3,782 [95% CI, 36.2 to 39.3]) and 16.2% (612/3,782 [95% CI, 15 to 17.4]), respectively. Of any %, 2+/3+ TC-positive tumors, 42.9% (612/1,428 [95% CI, 40.3 to 45.4]) were FGFR2b ≥10%, 2+/3+ TC positive. FGFR2b prevalence was not notably different within multiple patient and sample characteristics examined (age, sex, collection method [biopsy v resection], collection site, location of primary tumor, and geographic region). As of the data cutoff date, we report the largest prevalence assessment of FGFR2b protein overexpression in GC with more than one third (37.8%) of patients with GC exhibiting FGFR2b protein overexpression (any % TC, 2+/3+) by a validated IHC assay. Approximately 16% of patients had FGFR2b protein overexpression in ≥10% of TC. FGFR2b prevalence was similar across geographic regions and within defined patient and sample variables regardless of the level of expression.

Rare earth element (REE) speciation in municipal solid waste incineration ash

A robust and sustainable supply of rare earth elements (REE) is critically needed for clean-energy technologies, which has stimulated substantial interests in REE recovery from waste streams. Municipal solid waste incineration ash (MSWIA) was recently recognized as a potentially important REE resource, yet REE speciation in MSWIA remains poorly understood. This study employed synchrotron X-ray spectroscopy and microscopy techniques to elucidate the speciation of representative REE (Y, Ce, and Nd) in different MSWIA samples. Linear combination fitting of bulk X-ray absorption near edge structure (XANES) data indicated that Y-bearing Al/Fe oxides and phosphates are the primary Y-hosting phases. Micro-XANES of individual Y-containing particles identified by micro X-ray fluorescence (μXRF) mapping revealed notably different Y speciation at micro-scale from the bulk, consistent with the highly heterogeneous nature of MSWIA samples. The main REE-bearing phases in different size-fractionated MSWIA are similar: Y and Nd as oxides and xenotime/monazite, and Ce as apatite and monazite. Our results provide important insights for designing pre-screening processes (e.g., density separation) and optimizing extraction methods (e.g., pH, use of ligands) for cost-effective REE recovery from MSWIA.

Can Anyone Learn to Code? A Qualitative Study of Place-Based Information Technology Training Programs

The demand for workers with information technology skills is high, creating a challenge for today's workforce system to train enough people. In response, nonprofit organizations and workforce development agencies have developed place-based training programs for information technology jobs. The intricate nature of these jobs necessitates innovative training approaches that address both technical proficiency and cultural competence. Drawing from interviews and case studies, this analysis highlights the importance of rigorous prescreening processes, structured support for graduates transitioning to employment, and collaboration with employers in ensuring the success and sustainability of place-based information technology training programs. It is recommended that programs incorporate these elements into their design to effectively meet the demands of the labor market and foster diversity and inclusion within the technology sector.

Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers’ physiological measurements and mouth level exposure

Pharmacokinetic (PK) studies can provide essential information on abuse liability of nicotine and tobacco products but are intrusive and must be conducted in a clinical environment. The objective of the study was to explore whether changes in plasma nicotine levels following use of an e-cigarette can be predicted from real time monitoring of physiological parameters and mouth level exposure (MLE) to nicotine before, during, and after e-cigarette vaping, using wearable devices. Such an approach would allow an -effective pre-screening process, reducing the number of clinical studies, reducing the number of products to be tested and the number of blood draws required in a clinical PK study Establishing such a prediction model might facilitate the longitudinal collection of data on product use and nicotine expression among consumers using nicotine products in their normal environments, thereby reducing the need for intrusive clinical studies while generating PK data related to product use in the real world.An exploratory machine learning model was developed to predict changes in plasma nicotine levels following the use of an e-cigarette; from real time monitoring of physiological parameters and MLE to nicotine before, during, and after e-cigarette vaping. This preliminary study identified key parameters, such as heart rate (HR), heart rate variability (HRV), and physiological stress (PS) that may act as predictors for an individual’s plasma nicotine response (PK curve). Relative to baseline measurements (per participant), HR showed a significant increase for nicotine containing e-liquids and was consistent across sessions (intra-participant). Imputing missing values and training the model on all data resulted in 57% improvement from the original’learning’ data and achieved a median validation R2 of 0.70.The study is in its exploratory phase, with limitations including a small and non-diverse sample size and reliance on data from a single e-cigarette product. These findings necessitate further research for validation and to enhance the model's generalisability and applicability in real-world settings. This study serves as a foundational step towards developing non-intrusive PK models for nicotine product use.

Barriers to diverse clinical trial participation in Duchenne muscular dystrophy: Engaging Hispanic/Latina caregivers and health professionals

BackgroundDespite the increasing availability of clinical trials in Duchenne muscular dystrophy, racial/ethnic minorities and other populations facing health disparities remain underrepresented in clinical trials evaluating products for Duchenne. We sought to understand the barriers faced by Hispanic/Latino families specifically and underrepresented groups more generally to clinical trial participation in Duchenne.MethodsWe engaged two participant groups: Hispanic/Latino caregivers of children with Duchenne in the US, including Puerto Rico, and health professionals within the broader US Duchenne community. Caregiver interviews explored attitudes towards and experiences with clinical trials, while professional interviews explored barriers to clinical trial participation among socio-demographically underrepresented families (e.g., low income, rural, racial/ethnic minority, etc.). Interviews were analyzed aggregately and using a thematic analysis approach. An advisory group was engaged throughout the course of the study to inform design, conduct, and interpretation of findings generated from interviews.ResultsThirty interviews were conducted, including with 12 Hispanic/Latina caregivers and 18 professionals. We identified barriers to clinical trial participation at various stages of the enrollment process. In the initial identification of patients, barriers included lack of awareness about trials and clinical trial locations at clinics that were less likely to serve diverse patients. In the prescreening process, barriers included ineligibility, anticipated non-compliance in clinical trial protocols, and language discrimination. In screening, barriers included concerns about characteristics of the trial, as well as mistrust/lack of trust. In consent and recruitment, barriers included lack of timely decision support, logistical factors (distance, time, money), and lack of translated study materials.ConclusionsNumerous barriers hinder participation in Duchenne clinical trials for Hispanic/Latino families and other populations experiencing health disparities. Addressing these barriers necessitates interventions across multiple stages of the clinical trial enrollment process. Recommendations to enhance participation opportunities include developing clinical trial decision support tools, translating prominent clinical trials educational resources such as ClinicalTrials.gov, fostering trusting family-provider relationships, engaging families in clinical trial design, and establishing ethical guidelines for pre-screening potentially non-compliant patients.

Drone-based hybrid charging for multiple sensors: A distributionally robust optimization approach

The drone-based charging is emerging as a promising way to supply electricity to sensors that keep the Internet of Things working. One-to-one and one-to-many charging strategies can both be adopted. In this background, how to dispatch drones and decide the charging strategy becomes an important problem. In this paper, we first investigate the electricity consumption uncertainty during drone travel, and then develop a distributionally robust hover location and routing optimization model with hybrid charging strategies. After that, we transform the established model into a tractable mixed-integer linear programming model based on dual theory. In order to solve it, we introduce a pre-screening process based on greedy algorithm to select the candidate hover locations. Finally, we conduct extensive numerical experiments and sensitive analysis to verify the efficacy and advantages of the proposed method. We find that the optimized charging strategies and drone dispatching schemes can vary with different sensor distribution patterns. Valuable managerial insights are also provided for drone-based hybrid charging in practice.

Effects of systematic predictor selection for statistical downscaling of rainfall in Hawai'i

AbstractWhile there has been rapid advancement in the development and application of statistical downscaling methods for climate projection, determining the best predictive large‐scale climate information for the targeted local climate variable remains a challenge. The choice of predictor variables is one of the most influential steps of model development and has the potential to lead to varying results, contributing to the total uncertainty of future projections. Despite being a well‐known problem, predictor selection often does not receive adequate attention and the development of a straightforward and feasible prescreening process is needed to provide guidance to simple (e.g., linear regression) and complex (e.g., machine learning) modeling tools alike. In this project, Akaike information criterion (AIC) and leave one out cross validation are used to evaluate sets of predictor variables common in statistical downscaling models of precipitation (e.g., temperature, geopotential height, moisture transport). Because thousands of predictor sets were found to be competitive in their statistical skill in projecting rainfall, results suggest that an ensemble of predictor sets should be used to account for the resulting variance associated with predictor selection. This ensemble approach is applied to make improvements to the most recent statistical downscaling model developed for rainfall projection in Hawai'i. An ensemble validation study is performed by projecting future wet season rainfall in Hawai'i for scenario RCP4.5 using 17 CMIP5 GCMs with ensembles of highly ranked and poorly ranked predictor sets. Results show significantly less variance and improved agreement in the projected sign of precipitation change for the highly ranked predictor set ensemble. In conclusion, it is recommended that statistical downscaling models are run in an ensemble‐mode with multiple combinations of predictors instead of using a single model, allowing for additional quantification of model uncertainty and a “safety net” for when the true physically best‐suited predictor set is unknown.

Innovative recruitment strategies for a multi‐domain lifestyle intervention trial (FINGER‐NL) via the Dutch Brain Research Registry

AbstractBackgroundFinding eligible participants for dementia prevention trials is challenging, resulting in prolonged recruitment periods, underpowered studies, and excessive costs. The Dutch Brain Research Registry (DBRR) is an online platform to facilitate recruitment of research participants. We evaluated different recruitment strategies with the ambition to optimize recruitment for the multi‐domain lifestyle intervention trial FINGER‐NL.MethodDBRR registrants were prescreened for FINGER‐NL based on available profile information. Potentially eligible registrants received an automatic study invitation. Those interested were contacted for additional telephonic screening and, if eligible, included in the trial. Five recruitment strategies within 2022 were executed with the goal to increase the number of DBRR registrants: 1) a paid Facebook‐campaign, 2) referral via (patient)‐organizations, 3) newspaper articles, 4) live‐events, and 5) national television appearance (W.F&E.S.). Recruitment source was traced (online; UTM tags) and/or reported by the participant. For most recruitment strategies, we involved a nationally well‐known ambassador (E.S.) who advocates a healthy lifestyle to improve brain‐health (Figure 1). For each strategy, we described the number of individuals registered, eligible and included in FINGER‐NL. We calculated efficiency (%eligible/prescreened) as a reflection of the proportion of eligible participants reached, and effectiveness (%included/registered) as a reflection of the proportion of participants included in FINGER‐NLResultIn 2022, the DBRR recruited 11234 new registrants. The highest numbers of registrants and highest number of inclusions were reached with national television appearance and the Facebook‐campaign (Table 1). In particular, the Facebook advertisement with a lifestyle theme and well‐known ambassador outperformed all other advertisements in number of registrations (Figure 1). After initial prescreening, between 18‐35% of registrants were eligible. Most efficient were national television and the Facebook‐campaign. Between 4‐12% of new registrants were included in the trial. Most effective strategies were national television appearance and referrals via patient‐organizationsConclusionOur results show that mass‐media, i.e. tailor‐made Facebook‐campaigns, national television appearance and events, are efficient ways to reach large numbers of eligible participants, especially when well‐known ambassador(s) were included. Recruitment strategies with detailed information about in‐ and exclusion of the trial are most effective. The DBRR provides an efficient and effective recruitment and prescreening process for the FINGER‐NL trial.

OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis.

By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL https://oncoctminer.chosenmedinfo.com.

Machine Learning to Predict Rice Stock for Food Security: A Brief Study

The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” and “rice”. Only journal papers were considered eligible that were published within 2020 – 2023. Approximately 49 articles from scopus and google scholar database. It was selected for full content review after the pre-screening process. The results indicated that this topic pertains to different disciplines that favor convergence research at the international level. Prediction criteria related to food security were found to be the most inputs of prediction models. Machine learning models such as Random forest result in high accuracy in predict rice stock on 98%. This study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in predict rice stock and contributing to a more systematic research on this topic.

Abstract 9 Identification of Effective Changes to Workflow Triaging Criteria and Production Planning

Abstract Introduction The Cleveland Cord Blood Center (CCBC) analyzes fresh incoming cord blood units for the total nucleated cell (TNC) count. Units that meet the TNC criteria are processed for clinical banking and units not meeting the defined criteria are made available for research collaborations. CCBC sought to understand if adding additional pre-screening criteria would reduce post-manufacturing deferral rates and increase production of licensed units. Objectives To identify the main failure modes resulting in cord blood unit deferral for clinical banking post-manufacturing. To identify key factors resulting in unlicensed status of processed cord blood units. To implement changes to pre-screening strategies to reduce deferral rates and increase production of licensed units. Methods CCBC created four cross-departmental teams to evaluate the main reasons for deferrals post-manufacturing and to evaluate travel-risk history. Each committee had representation from collections, quality, and laboratory staff. Data was evaluated that was collected between January and June 2022. Results The committees identified that 19% of all deferrals post-manufacturing were related to low CD34 count and/or viability and that 17% of all processed units were unlicensed due to travel to Zika related areas. In March 2023, pre-screening criteria was updated to include a pre-processed CD34 count and viability and a pre-screening of the donating mother for travel history after consenting and prior to manufacturing. Units that fulfilled both the TNC and CD34 cut offs and did not have a high-risk travel history were processed for clinical banking. Data evaluated from March and April 2023 indicated that the deferral rates for low viable CD34 counts post-manufacturing went from 19% down to 0% and the percentage of high-risk travel units manufactured was reduced from 17% to 7%. Discussion Review of failure modes identified two main factors to guide improved triage efficiency. By implementing a pre-processing CD34 count, the larger TNC and CD34 cord blood units were identified for banking for clinical use. By using a pre-screening process at collection sites, high-risk travel donations were identified prior to processing. In the two-month timeframe evaluated, the overall deferral rates were reduced by roughly 7% and post-manufacturing licensure status increased from 83% to 93%.

Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models

This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk assessment. For this purpose, experimental primary and ultimate biodegradation rates of high consistency were compiled for 173 organic compounds. A significant number of descriptors were calculated with a collection of quantum/computational chemistry software and tools to achieve comprehensive representation and interpretability. Following a pre-screening process, multiple QSBR models were developed for both primary and ultimate endpoints using three algorithms: extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). Furthermore, a unified QSBR model was constructed using the knowledge transfer technique and XGBoost. Results demonstrated that all QSBR models developed in this study had good performance. Particularly, SVM models exhibited high level of goodness of fit (coefficient of determination on the training set of 0.973 for primary and 0.980 for ultimate), robustness (leave-one-out cross-validated coefficient of 0.953 for primary and 0.967 for ultimate), and external predictive ability (external explained variance of 0.947 for primary and 0.958 for ultimate). The knowledge transfer technique enhanced model performance by learning from properties of two biodegradation endpoints. Williams plots were used to visualize the application domains of the models. Through SHapley Additive exPlanations (SHAP) analysis, this study identified key features affecting biodegradation rates. Notably, MDEO-12, APC2D1_C_O, and other features contributed to primary biodegradation, while AATS0v, AATS2v, and others inhibited it. For ultimate biodegradation, features like No. of Rotatable Bonds, APC2D1_C_O, and minHBa were contributors, while C1SP3, Halogen Ratio, GGI4, and others hindered the process. Also, the study quantified the contributions of each feature in predictions for individual chemicals. This research provides valuable tools for predicting both primary and ultimate biodegradation rates while offering insights into the mechanisms.

Centralizing prescreening data collection to inform data-driven approaches to clinical trial recruitment

BackgroundRecruiting to multi-site trials is challenging, particularly when striving to ensure the randomized sample is demographically representative of the larger disease-suffering population. While previous studies have reported disparities by race and ethnicity in enrollment and randomization, they have not typically investigated whether disparities exist in the recruitment process prior to consent. To identify participants most likely to be eligible for a trial, study sites frequently include a prescreening process, generally conducted by telephone, to conserve resources. Collection and analysis of such prescreening data across sites could provide valuable information to improve understanding of recruitment intervention effectiveness, including whether traditionally underrepresented participants are lost prior to screening.MethodsWe developed an infrastructure within the National Institute on Aging (NIA) Alzheimer’s Clinical Trials Consortium (ACTC) to centrally collect a subset of prescreening variables. Prior to study-wide implementation in the AHEAD 3–45 study (NCT NCT04468659), an ongoing ACTC trial recruiting older cognitively unimpaired participants, we completed a vanguard phase with seven study sites. Variables collected included age, self-reported sex, self-reported race, self-reported ethnicity, self-reported education, self-reported occupation, zip code, recruitment source, prescreening eligibility status, reason for prescreen ineligibility, and the AHEAD 3–45 participant ID for those who continued to an in-person screening visit after study enrollment.ResultsEach of the sites was able to submit prescreening data. Vanguard sites provided prescreening data on a total of 1029 participants. The total number of prescreened participants varied widely among sites (range 3–611), with the differences driven mainly by the time to receive site approval for the main study. Key learnings instructed design/informatic/procedural changes prior to study-wide launch.ConclusionCentralized capture of prescreening data in multi-site clinical trials is feasible. Identifying and quantifying the impact of central and site recruitment activities, prior to participants signing consent, has the potential to identify and address selection bias, instruct resource use, contribute to effective trial design, and accelerate trial enrollment timelines.

Peer review declaration

To ensure quality and professional ethics a peer review mechanism has been adopted. The reviewers (at least two for each paper) received the manuscript with all the details of the authors and a form, prepared for the conference, with certain fields for comments and recommendations. The authors, at the next step, received anonymous reviews with the following possible recommendations: accept as is/minor revisions/major revisions/reject. The papers were revised by the authors accordingly and then forwarded to the same reviewers for final recommendation. The final decision was taken by the editors.Conference submission management system:The papers were submitted to easy-chair, Morressier and an e-mail, handled by the editors. The handling editor managed the correspondence between reviewers and authors.Number of submissions received: 205Number of submissions sent for review: 90Number of submissions accepted: 56Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 27.31Average number of reviews per paper: 3Total number of reviewers involved: 73Any additional info on the review process:A pre-screening process has been adopted to comply with the quality of technical content under the following steps:1. Screening of the papers has been done by the editors on the originality of the content, similarity index, and technical area. If the paper has a similarity index of less than 20% and is related to the theme of the conference, reviewers have been assigned by the editors.2. If the technical area of the submitted paper is not related to the theme of the conference, the paper has been sent back to the authors by the editors.3. If the similarity index is more than 20%, the paper has also been sent back to the authors for revision.Contact person for queries: Dr. Vikesh Gupta, Associate Professor and Head, Sushila Devi Bansal College, Indore, Madhya Pradesh, IN,E-mail: conference@sdbc.ac.in, M: +91 8435431353

P664 Analysis of Clinical Trial Screen Failures in IBD: Real World Results from the IOIBD

Abstract Background Recruitment rates for phase 2b/3 randomized controlled trials (RCTs) in IBD have substantially dropped over time. Several steps are required prior to successful patient randomization. Initially the physician must propose a trial to a potentially eligible patient during a pre-screening process (step 1). This is followed by patient’s acceptance or refusal (step 2). Finally, after informed consent the patient undergoes trial screening to ensure they meet all eligibility criteria (step 3). Evaluating each step separately, this study aims to assess reasons why IBD patients are not included in RCT and patients’ outcome after screen failure (SF). Methods All IOIBD member physicians (n=58) were invited to participate. To assess steps 1 and 2, consecutive IBD patients in relapse for whom a treatment change was required were prospectively included over a 4-week period. Reasons that prevented the IBD physician offering a sponsored multicenter phase 2b/3 RCT (step 1) and reasons why the patient accepted or refused to participate (step 2) were assessed through a physician and a patient survey, respectively. Reasons for SF (step 3) from the last 6 months, including the 4 weeks of steps 1-2, were collected retrospectively. Results A total of 104 (59 male, 62 CD, mean age of 37.2 years) and 102 patients (58 male, 63 CD, mean age of 40.6 years) from 12 centers were included in steps 1-2 and 3, respectively (Tables 1 and 2). Among 104 patients in relapse for whom a treatment change was required, 41 (39.4%) were offered a RCT. Of the 28 who consented to RCT, 5 failed their screening (SF rate of 17.9%) and 23 were included. Main reason that prevent IBD physicians from offering an RCT (step 1) are shown in Figure 1. After receiving information about RCT, major reasons why patients accepted or refused to participate included the trust they had in their IBD specialist and the risk of being assigned to a placebo, respectively (step 2). Regarding 102 patients included in step 3, main reasons of SF were insufficient disease activity (n=37), concurrent infection (n=15) and dropout (n=12) (Figure 2). Half of SFs could have been avoided by thorough prescreening. After SF, 51 patients were treated with commercially available therapy, 14 were rescreened for the same RCT (after resolution of the issue leading to SF), no treatment was required for 14, 10 were referred to surgery and 6 were screened for another RCT (the outcome was unknown for 7). Conclusion This first multicentric study reported a SF rate of 17.9%. Insufficient disease activity and the risk of assigning the patient to a placebo seem to be barriers to inclusion. Half of SFs could have been avoided by better pre-screening. After SF, most of patients were treated with commercially available therapy.

Perspective on machine learning in energy material discovery

Perspective on machine learning in energy material discovery