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Food security is a pressing global concern, particularly highlighted by the United Nations Sustainable Development Goal 2 (SDG 2), which focuses on enhancing the productivity and incomes of smallholder farmers. In the Middle East and North Africa (MENA) region, horticultural crops are increasingly threatened by pests and diseases, exacerbated by climate change. Local farmers often lack the necessary expertise to effectively manage these issues, resulting in significant reductions in both yield and quality of their crops. This study seeks to develop an accessible mobile crop diagnosis application. By utilizing machine learning and deep learning technologies, the app is designed to help MENA farmers quickly and accurately identify and treat crop disorders. We used Open Data Kit (ODK) to gather a large dataset of crop images required to train deep learning models. These models, built on open-source deep learning architectures, were designed to classify 21 different leaf disorders, including diseases, pests, and nutritional deficiencies. The system was implemented in both a web app and an Android mobile app. Our deep learning models demonstrated an overall accuracy of 94 % in diagnosing plant disorders. The app, Doctor Nabat, includes a decision support system that offers treatment options in the three primary languages spoken in the MENA region. Doctor Nabat is an effective and scalable tool for enhancing crop management in the MENA region, promoting food security by minimizing crop losses through improved pest and disease diagnosis and treatment strategies.

Background: Collecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. Methods: CHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. Results: For non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p

Summary: Irrigation drives water consumption in agriculture, essential for crop productivity and conserving water resources. Smart irrigation optimizes water usage, enhances yields, and supports sustainability. Integrating Information and Communication Technologies (ICT) and the Internet of Things (IoT) shows potential for reducing water consumption, but hardware, software, and connectivity solutions are still developing. Low-Power Wide-Area Access (LPWA) technologies enable energy-efficient IoT applications with long-range communication. LoRaWAN ensures secure data transmission up to 15 km using a single gateway. This paper presents a LoRaWAN-based prototype irrigation platform developed within the AREThOU5A framework, implemented in a 22-hectare olive grove with Kalamon olives in Greece. The system monitors soil moisture levels using IoT end nodes with low-cost microcontrollers and embedded sensors. Each node measures moisture at depths of 30 cm, 60 cm, and 90 cm, operating on battery power for months and communicating via LoRaWAN. Data is transmitted through a LoRaWAN gateway to a cloud-based infrastructure using 4G connectivity, allowing real-time access via online dashboards. Collected data will inform smart irrigation strategies, maximizing water savings and ensuring reliable connectivity. A robust software development system has integrated IoT subsystems for olive grove management, centralizing measurement data in a web-accessible database. This enables real-time monitoring, trend analysis, and cultivation optimization. The system is hosted on a public cloud platform, using Docker for flexible deployment and data management. In the olive grove in Peta, a specialized InfluxDB routine optimizes water use, operating effectively for two years. An annual dashboard highlights key data points, including soil moisture, temperature readings, and health diagnostics. Water consumption was reduced by 42 % in 2020 and 25 % in 2021. An identification application secures user data management and supports sustainability.

Background: Skin symptom burden, varying with patient populations, may not be readily observed by clinicians, resulting in incomplete appreciation of total skin disease burden. Objective: The purpose of this study was to define patient itch burdens and associated health-related QOL affecting different patient demographics and to identify potential population health disparities. Methods: This is a cross-sectional, secondary data analysis of data captured using an automated routine electronic previsit survey completed by patients who visited Emory Healthcare Dermatology clinic between March 2021 and October 2022 (6532 patient visits). Descriptive statistics and ordered logit regression analyses were used to examine the prevalence and intensity of itch and the impacts of itch on QOL. Results: Overall itch burden increases as age increases; females and African Americans experienced more itch burden than males and other racial groups. Itch places significant symptom, emotional, and functional burdens on patients’ QOL, impacts that are independent of patients’ demographics. Limitations: The data collected were from a single dermatology practice and may not be reflective of other practices or populations. Conclusion: Dermatology previsit surveys are feasible in examining the significant pruritus burden, especially for older individuals, females, and African Americans with chronic skin conditions.

Summary: Background: Clinical trials and registry studies are essential for advancing research and developing novel treatments. However, these studies rely on manual entry of thousands of variables for each patient. Repurposing real-world data can significantly simplify the data collection, reduce transcription errors, and make the data entry process more efficient, consistent, and cost-effective. Methods: We developed an open-source computational pipeline to collect laboratory and medication information from the electronic health record (EHR) data and populate case report forms. The pipeline was developed and validated with data from two independent pediatric hospitals in the US as part of the Long-terM OUtcomes after Multisystem Inflammatory Syndrome In Children (MUSIC) study. Our pipeline allowed the completion of two of the most time-consuming forms. We compared automatically extracted results with manually entered values in one hospital and applied the pipeline to a second hospital, where the output served as the primary data source for case report forms. Findings: We extracted and populated 51,845 laboratory and 4913 medication values for 159 patients in two hospitals participating in a prospective pediatric study. We evaluated pipeline performance against data for 104 patients manually entered by clinicians in one of the hospitals. The highest concordance was found during patient hospitalization, with 91.59% of the automatically extracted laboratory and medication values corresponding with the manually entered values. In addition to the successfully populated values, we identified an additional 13,396 laboratory and 567 medication values of interest for the study. Interpretation: The automatic data entry of laboratory and medication values during admission is feasible and has a high concordance with the manually entered data. By implementing this proof of concept, we demonstrate the quality of automatic data extraction and highlight the potential of secondary use of EHR data to advance medical science by improving data entry efficiency and expediting clinical research. Funding: NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.

Background and Aim: Qualitative research plays an important role in improving nursing knowledge. Understanding the concept of saturation is essential to conducting rigorous qualitative research that contributes to evidence-based practice. The purpose of this study is to clarify the concept of saturation in qualitative research. Method: Evolutionary concept analysis was performed. A literature search was conducted using a variety of online databases for the years 2005- 2023. In total, 33 articles and books were analyzed using thematic analysis to identify the attributes, antecedents and consequences of saturation. The validity of the data was obtained by examining the analysis process by two independent researchers. Results: Saturation in qualitative research is a context-dependent, subjective process that requires detailed systematic analysis. Saturation is used in four ways in qualitative research: theoretical saturation, data saturation, code or thematic saturation, and meaning saturation. The antecedents of saturation were classified into two categories: study related factors and researcher related factors. The consequences of saturation were identified as: ensuring credibility and quality in qualitative research and time, energy and budget saving. Conclusion: This concept analysis serves to enhance the understanding of the concept of saturation, thereby offering valuable resources for qualitative researchers. By gaining a profound comprehension of saturation and its various types, researchers can ensure the validity of their studies while also optimizing time and resource allocation by avoiding redundant data collection. Future investigation warranted to elucidate how factors associated with reaching saturation impact estimations sample size.

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, we study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors (LBDs). This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that real-time data performance is ensured It has been proven that the RDCL problem is NP-hard. To address this, we initially focus on studying two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Subsequently, we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL problem. Finally, the proposed algorithm is verified by extensive simulations.

According to the World Health Organisation (WHO), brain stroke is the second leading cause of death and disability worldwide in terms of key ICD (International Cause of Death) categories. Every 4 min, on average, a stroke-related mortality occurs in India alone.Machine learning (ML) has developed into a potent tool in healthcare settings, providing individualised therapeutic treatment for stroke patients. ML gives an accurate and speedy prediction conclusion. However, it is clear from the research that better training quality is required, along with the identification of the best classifiers for combining to improve prediction accuracy. In this article, we have suggested a framework to aid diagnosis and Clinical treatment for patients with stroke.In this study, we introduced a system called Enhanced Stroke Prediction Ensemble using Stacking Methodology (ESPESM) in Intelligent Sensors, which expands on the previously successful SPE using Stacking and leverages a hybrid method considering feature engineering and ensemble classification.The fundamental classifiers for the proposed stacking prediction model were Random Forest (RF), K-Nearest Neighbours (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Gradient Boosting Classifier (GBC), Decision Tree Classifier, Stochastic Gradient Descent(SGD), and Bernoulli NB(BNB),while Random Forest was selected as the meta learner. Accuracy, Precision, Recall, and F1 score have been used to assess the suggested stacking model's performance.The most accurate models from a pool of potential brain stroke prediction models are selected, and these models are then layered to create an ensemble model. According to prior established literature, stacked ensembles often give a more accurate prediction performance than simple individual models or average ensemble addition. The framework is accomplished by utilising and adapting previously existing methods. They are referred to as “Hybrid Ensemble and Feature Engineering for Stroke Prediction (HEFE-SP)'' and “Hybrid Measures Approach for Feature Engineering (HMA-FE)''.Additionally, when a dataset is imbalanced, as it is in most medical datasets, the Matthews Correlation Coefficient (MCC) has also been utilised for more accurate evaluation. The accuracy and MCC values of the proposed stacking model, which are 98 % and 94 % respectively, show that it outperforms solo classifiers.

Background: Registry-based trials have the potential to reduce randomized clinical trial (RCT) costs. However, observed cost differences also may be achieved through pragmatic trial designs. A systematic comparison of trial costs across different designs has not been previously performed. Methods: We conducted a study to compare the current Steroids to Reduce Systemic inflammation after infant heart surgery (STRESS) registry-based RCT vs. two established designs: pragmatic RCT and explanatory RCT. The primary outcome was total RCT design costs. Secondary outcomes included: RCT duration and personnel hours. Costs were estimated using the Duke Clinical Research Institute's pricing model. Results: The Registry-Based RCT estimated duration was 31.9 weeks greater than the other designs (259.5 vs. 227.6 weeks). This delay was caused by the Registry-Based design's periodic data harvesting that delayed site closing and statistical reporting. Total personnel hours were greatest for the Explanatory design followed by the Pragmatic design and the Registry-Based design (52,488 vs 29,763 vs. 24,480 h, respectively). Total costs were greatest for the Explanatory design followed by the Pragmatic design and the Registry-Based design ($10,140,263 vs. $4,164,863 vs. $3,268,504, respectively). Thus, Registry-Based total costs were 32 % of the Explanatory and 78 % of the Pragmatic design. Conclusion: Total costs for the STRESS RCT with a registry-based design were less than those for a pragmatic design and much less than an explanatory design. Cost savings reflect design elements and leveraging of registry resources to improve cost efficiency, but delays to trial completion should be considered.

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

Glucose isomerase (GI) is a crucial enzyme in industrial processes, including the production of high-fructose corn syrup, biofuels, and other renewable chemicals. Understanding the mechanisms of GI inhibition by GI inhibitors can offer valuable insights into enhancing production efficiency. We previously reported the subatomic resolution structure of Streptomyces rubiginosus GI (SruGI) complexed with a xylitol inhibitor, determined at 0.99 Å resolution, was reported. Structural analysis showed that the xylitol inhibitor is partially bound to the M1 binding site at the SruGI active site, enabling it to distinguish the xylitol-bound and -free state of SruGI. This structural information demonstrates that xylitol binding to the M1 site causes a conformational change in the metal binding site and the substrate binding channel of SruGI. Herein, detailed information on data collection and processing procedures of the subatomic resolution structure of the SruGI complexed with xylitol was reported.

Despite numerous tutorials and standards written to the technical community on X-ray photoelectron spectroscopy (XPS), difficulties with data acquisition, analysis, and reporting persist. This work focuses on common errors in XPS that are frequently observed in the scientific literature and their sources. Indeed, this work covers: (i) XPS data collection, initial data analysis, and data presentation, (ii) Handling XPS backgrounds, (iii) Common errors in XPS peak fitting, and (iv) XPS data presentation and reporting. Graphical examples of errors and appropriate ways of handling data and correcting errors are provided. Additional readings are listed for greater in-depth exploration of the subjects discussed.

We investigated the feasibility of an interactive voice response (IVR) survey in Tanzania and compared its prevalence estimates for tobacco use to the estimates of the 'Global Adult Tobacco Survey (GATS) 2018′. IVR participants were enrolled by random digit dialing. Quota sampling was employed to achieve the required sample sizes of age-sex strata: sex (male/female) and age (18–29-, 30–44-, 45–59-, and ≥60-year-olds). GATS was a nationally representative survey and used a multistage stratified cluster sampling design. The IVR sample’s weights were generated using the inverse proportional weighting (IPW) method with a logit model and the standard age-sex distribution of Tanzania. The IVR and GATS had 2362 and 4555 participants, respectively. Compared to GATS, the unweighted IVR sample had a higher proportion of males (58.7 % vs. 43.2 %), educated people (secondary/above education: 43.3 % vs. 21.1 %), and urban residents (56.5 % vs. 40 %). The weighted prevalence (95 % confidence interval (CI)) of current smoking was 4.99 % (4.11–6.04), 5.22 % (4.36–6.24), and 7.36 % (6.51–8.31) among IVR (IPW), IVR (age-sex standard), and GATS samples, respectively; the weighted prevalence (95 % CI) of smokeless tobacco use was similar: 3.54 % (2.73–4.57), 3.58 % (2.80–4.56), and 2.43 % (1.98–2.98), respectively. Most differences in point estimates for tobacco indicators were small (

The current carbon intensity indicator (CII) formulation has raised some fundamental concerns among maritime experts. The annual emission rate (AER) lacks sufficient sensitivity in reflection of cargo tonnage and ship speed, and further, calculation of transport work is subject to dispute. In addition, there are concerns regarding the reliability of the annual data collection. Two proposals are presented in this study in order to address these issues: first, modification in the CII formula and a new technique to calculate the average CII, and second, a mechanism of voyage-based data collection and verification. Case studies and real noon reports were used to demonstrate the compatibility of the modified CII formulae and their ability to encourage ship operators to slow steaming. Moreover, case studies demonstrated ineffectiveness of the engine power limits (EPL), and conflicting results when energy efficiency operational indicator (EEOI) and AER were applied to the same dataset.

Medical artificial intelligence (AI) and big data technology have rapidly advanced in recent years, and they are now routinely used for image-based diagnosis. China has a massive amount of medical data. However, a uniform criteria for medical data quality have yet to be established. Therefore, this review aimed to develop a standardized and detailed set of quality criteria for medical data collection, storage, annotation, and management related to medical AI. This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

Layered elastic theory (LET) was performed by Burmister. It helped to build mechanistic – empirical (M-E) pavement design. In this study, three different approaches were used to predict Cumulative Equivalent single axle load (C-ESAL) over the design period. Two were based on M-E and one was empirical. In each of these cases, standard axle loads were used as well as weight limits and vehicle classification, according to their axle configurations (single, tandem, tridem). Traffic data came from annual traffic census campaigns over the past ten years. Gross vehicle weight (GVW) and axle weight (AW) data came from a fixed weighing station performed during 31 days in 2020. Two road axis were considered: One having a weighing station (reference road) and one under technical studies (specific road). Traffic road data were used to perform regression analyses and predictions. AW and GVW helped to calculate Axle load equivalency factors (ALEF) and Truck equivalency factors (TEF) on the reference road. These values were projected on the specific road. Frequency distribution, gross vehicle weight distribution, axle load distribution of heavy vehicles are applied on the reference road. We performed overload AW and overload GVW analyses. Comparisons were done for the three approaches and an evaluation of technical studies was proposed, including traffic and AW monitoring and management systems. This work came as a basis for the transposition of M-E calculation of traffic inputs, more accurate and used over the passed fifty years, in Higher Income countries, called AASHTO method for USA, LCPC-SETRA method for France, to Cameroon and Sub-sahara African countries, that have been using empirical generation of traffic inputs over the same period, called CEBTP method.

The main objective of this study is to optimize the performance of a wireless sensor network (WSN) in terms of energy consumption and data routing delay using evolutionary metaheuristics. A WSN mobile sink-based routing method in which the sink moves to certain selected rendezvous nodes for data collection is used to address the hot-spot problem. However, there are two challenges, namely, clustering and mobile sink shortest trajectory traversal, which significantly affect the network energy consumption, lifetime, and delay. To achieve the goal of this study, first, a formal model is presented to solve the optimization problem of determining the optimal number of clusters and corresponding cluster heads, which are taken as rendezvous nodes accessed by the mobile sink considering the network energy consumption, intracluster communication, and transmission delay. Second, a discrete differential evolution algorithm is proposed to solve the formulated optimization problem. Third, an ant colony optimization-based algorithm is proposed to construct the shortest path for the mobile sink to traverse the selected rendezvous nodes. The experimental evaluations show that the proposed strategy significantly improves cluster head selection, balances the cluster members, increases the network lifetime by 54%, decreases the transmission delay by 63%, and reduces energy consumption by 47% compared to several routing strategies in the literature.

Large Digital Imaging and Communications in Medicine (DICOM) datasets are key to support research and the development of machine learning technology in radiotherapy (RT). However, the tools for multi-centre data collection, curation and standardisation are not readily available. Automated batch DICOM export solutions were demonstrated for a multicentre setup. A Python solution, Collaborative DICOM analysis for RT (CORDIAL-RT) was developed for curation, standardisation, and analysis of the collected data. The setup was demonstrated in the DBCG RT-Nation study, where 86% (n = 7748) of treatments in the inclusion period were collected and quality assured, supporting the applicability of the end-to-end framework.

Objective: To identify the challenges and proposed solutions for researchers dealing with participants’ death during data collection in the older adult population; and to describe what is known about researchers’ strategies to deal with the death of older adult participants when conducting data collection. Methods: Searches were performed using CINAHL, Scopus, PubMed on Ebscohost, and PsycINFO databases from inception to November 2022. A total of 4158 articles were identified by the online search with the data screened independently by two reviewers. Results: Three articles were analysed with a total of 69 researchers from Australia, Belgium, and USA. The main challenges were identified as emotional burden, trespassing the boundaries of ethical responsibility, and lack of skills and training. Whilst the main proposed solutions were researcher-participant relationship, self-care, and skills and training. Conclusion: Researchers are affected personally and professionally by the experience of death when dealing with older adult participants. This review fills a serious gap in the literature on the challenges and proposed solutions for researchers when dealing with death during data collection with older adults. This review may serve as a reference guide to help researchers when dealing with death whilst collecting data in the older adult population.

The Internet and the Internet of Things (IoT) connect the world inside and outside the network through online and offline communication, which forms the original single network virtual platform. The integration of IoT, virtual reality and artificial intelligence can provide intelligent WebVR interaction for various application fields. However, we need to solve the problems of data lightweight, real-time data transmission based on the IoT, and visualization of scene data on the Web. This paper uses the IoT and virtual reality technology to propose a virtual reality scene modeling method based on the IoT platform, as well as the database dynamic loading management method and device of the virtual reality system, which includes building the database, storing the involved data structures for various scenes and functions in the virtual reality system. The time–space IoT engine is connected with the integrated service sharing center. The IoT platform collects data through the IoT sensor system and provides support for the application platform through the integrated service sharing center. The proposed method can meet the needs of large-scale data acquisition and modeling, shorten the loading time, improve the efficiency, and save the wasted computing resources and memory.

Energy conservation is one of the main challenges hindering the advancement of Wireless Sensor Networks (WSNs) in the Internet of Things (IoT). Although various approaches including probabilistic, deterministic, and mixed model based solutions have been proposed to solve the issue of energy conservation in cluster based WSNs, most of them do not consider the node’s heterogeneity in terms of energy as well as transmission rate during the creation or selection of clusters. In this paper, a new Fairness Aware Clustering Scheme (FACS) for heterogeneous WSN is proposed, where we consider heterogeneity of the WS N in terms of transmission rate and energy. In contrast to existing schemes, the proposed FACS provides a set of polices that take care of round operations and improve the network performance and lifetime. In FACS, fuzzy logic is employed to benefit from the heterogeneity of the nodes and to determine the utility of each node. Through extensive simulation, we show that FACS performs efficiently and it outperforms the existing state-of-the-art approaches in terms of network lifespan, energy, and fairness.

Background: Parent-report questionnaires are a common method of generating data on child outcomes in mental health studies. A second report from another person who knows the child (co-respondent) is implemented to reduce bias and increase objectivity. The success of this approach is dependent on the engagement of co-respondents, which can be difficult. Financial incentives are used to increase data return in clinical trials, and to promote referral rates in online marketing. This protocol describes the use of an embedded randomised controlled trial (RCT) to investigate the effect of financial incentives on rates of co-respondent data completion. In the host RCT (of an online intervention designed to reduce the impact of a parent's anxiety on their child) index participants (i.e. parents) are asked to invite a co-respondent to complete measures on the index child. This study will test the hypothesis that providing monetary incentives to index participants will increase the outcome measure completion rate of co-respondents. Methods: Embedded RCT of two parallel groups. Participants in the intervention arm will be sent a £10 voucher if their chosen co-respondent completes online baseline measures. Participants in the control arm will not be offered payment regardless of their chosen co-respondent's behaviour. 1754 participants will take part. Analysis will compare co-respondent outcome measure completion rates between the two arms at baseline and follow-up. Conclusion: Findings from this study will provide evidence on the impact of offering payment to index participants on return rates of co-respondent data. This will inform resource allocation within future clinical trials.

Purpose: The objective of this study is to investigate the predictive ability of machine learning models for imbalanced outcomes from national survey data without the use of sampling weights., Methods: We evaluated the predictive performance of machine learning models on imbalanced outcomes from the US National Health and Nutrition Examination Survey (USNHANES) without using sampling weights. Four machine learning models (support vector machine, random forest, least absolute shrinkage and selection operator regression, and deep neural network) were compared with a logistic model that incorporates the survey's complex sampling design. Three resampling methods (oversampling, undersampling, and combined) were used to address class imbalance during the model training process., Results: For all models, the balanced accuracy was similar (ranging from 0.72 to 0.76) and the specificity was smaller than sensitivity except for random forest. The support vector machine and neural networks performed best with sensitivity (ranging from 0.79 to 0.83), while the random forest had the largest specificity (ranging from 0.86 to 0.96), with one exception. PR-AUC scores and Brier scores were low ranging from 0.2529 to 0.3313 (lower scores worse) and 0.1005-0.3245 (lower scores better), respectively CONCLUSIONS: The machine learning models had overall similar predictive capacity to the recommended methods which integrate the complex sampling design for the prediction of osteoarthritis occurrence with USNHANES., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Emily Leary, PhD reports a relationship with Journal of ISAKOS that includes: board membership, funding grants, and speaking and lecture fees. Emily Leary, PhD reports a relationship with EVLVE Analytics and Consulting LLC that includes: consulting or advisory and employment. None. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Background: With the rise in salience of the concept of gender identity, changes are being made to language and data collection with major implications for women's health research and equality. Specifically, language referring to women is being desexed and data collection on sex diminishing. In 2023, Australia's National Health and Medical Research Council (NHMRC) undertook public consultations on two draft guidance documents discussing use of the words 'woman'/'women' when describing the involvement of pregnant women in research, and sex and gender identity data collection. We collaborated in writing and gathering support for responses to both consultations., Discussion: We advocated retaining sexed usage of woman/women when sex was relevant, emphasising that addressing sexism and the female data gap requires identifying women as a group and emphasised the need to avoid confusion, dehumanisation, and exclusion of disadvantaged groups. We expressed concern that data collection on gender identity is supplanting that on sex, and sex data is not being accurately collected. We recommended the NHMRC prioritise data accuracy, guide researchers on when and how to collect sex data, and recognise that individuals do not universally apply gender identity to themselves. These issues have international relevance as pressure to desex language and prioritise gender identity data is occurring world-wide. The NHMRC has now finalised its data collection guidance, unfortunately our concerns were largely ignored., Conclusion: Researchers and clinicians globally must urgently participate in policy discussion regarding the importance of sexed language and accurate sex data, to protect individual and population health and data and research integrity., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Plant protection products (PPPs) undergo rigorous safety assessments. In Europe, non-dietary risk assessments for operators, workers, bystanders and residents are highly conservative as this area of exposure science has historically been data poor. CropLife Europe (CLE) companies have collaborated to generate new data and pool existing data to refine the approaches prescribed by the European Food Safety Authority (EFSA) guidance on non-dietary exposure (2022). This article summarises key activities, beginning with the development of the Agricultural Operator Exposure Model (AOEM) and covers projects which refine current approaches to bystander, resident and re-entry worker assessment, including the Bystander Resident Orchards Vineyards (BROV) project, improvements to the Bystander and Resident Exposure Assessment Model for spray drift (BREAM), proposals for refined vapour inhalation assessments, and a meta-analysis of Dislodgeable Foliar Residue (DFR) data. A study quantifying the benefits of using closed transfer systems, an appraisal of the inherent compounded conservatism in current risk assessment paradigms and the development of a new seed treatment model by the SeedTROPEX taskforce are also introduced. These industry-led activities underscore the critical role of non-dietary exposure in the registration process for PPPs and reflect an ongoing commitment to provide farmers with effective crop protection solutions while ensuring safety., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. All authors are employees of companies that conduct and evaluate risk assessments for regulatory purposes in the context of authorization and marketing of their companies' products. They contribute as scientific experts to the industry association CropLife Europe for evaluation and development of the state-of-the-art methodology., (Copyright © 2024. Published by Elsevier Inc.)

The National Death Index (NDI) by the Centers for Disease Control and Prevention and Death Master File (DMF) by Social Security Administration are the two most broadly utilized data files for mortality outcomes in clinical research. NDI's high costs and the elimination of protected death records from California in DMF calls for alternative death files. The recently emerged California Non-Comprehensive Death File (CNDF) serves as an alternative source for vital statistics. This study aims to evaluate the sensitivity and specificity of CNDF compared to NDI.Of 40,724 consented subjects in the Cedars-Sinai Cardiac Imaging Research Registry, 25,836 eligible subjects were queried through the NDI and the CDNF. After exclusion of death records to establish the same temporal and geographic availability of data, NDI identified 5,707 exact matches, while CNDF identified 6,051 death records. CNDF had a sensitivity of 94.3% and specificity of 96.4% compared to NDI exact matches. NDI also produced 581 close matches: all were verified as deaths by CNDF through matching death date and patient identifiers. Combining all NDI death records, CNDF had a sensitivity of 94.8% and specificity of 99.5%. CNDF is a reliable source for obtaining mortality outcomes and providing additional mortality validation. The use of CNDF can aid and replace the use of NDI in the state of California.

Autonomous Vehicles (AVs) collect a vast amount of data during their operation (MBs/sec). What data is recorded, who has access to it, and how it is analysed and used can have major technical, ethical, social, and legal implications. By embedding Responsible Innovation (RI) methods within the AV lifecycle, negative consequences resulting from inadequate data logging can be foreseen and prevented. An RI approach demands that questions of societal benefit, anticipatory governance, and stakeholder inclusion, are placed at the forefront of research considerations. Considered as foundational principles, these concepts create a contextual mindset for research that will by definition have an RI underpinning as well as application. Such an RI mindset both inspired and governed the genesis and operation of a research project on autonomous vehicles. The impact this had on research outlines and workplans, and the challenges encountered along the way are detailed, with conclusions and recommendations for RI in practice.

There are numerous controversies in research exploring personality dynamics and intrapsychic processes, e.g. insufficient insight provided by available measures such as self-report questionnaires. As a consequence, new methods are developed. Some of the recent theories indicate that self-esteem is not a stable personality trait, but a dynamic construct fluctuating as a result of (mostly) social interactions. I present a semi-structured interview protocol as a method of data collection which can provide rich verbal and non-verbal material referring to self-esteem regulation. Analysis system is not included as there can be many different approaches to use collected data, e.g. qualitative content analysis or narrative inquiry methods. In this paper, I present exemplary statements of participants corresponding to every part of the interview. The examples are explained considering theoretical background. Finally, the strengths and limitations of presented method are discussed, as well as possible research areas to explore with it.

Twitter is an increasingly popular platform for understanding and analyzing human sociobehavioral dynamics, information diffusion, and public sentiment largely because of its high volume of content and relative ease of access. This has been particularly true in the field of crisis informatics, in which tweet content and social networks are utilized to study a community's response to a disaster or crisis event. Twitter's Terms of Service, however, restrict the quantity and types of data which can be collected from the platform. Accordingly, there are multiple ways to retrieve data from Twitter with no consensus among researchers as to standard data collection procedures. In this work, we compare two Tweet datasets gathered around Hurricane Harvey—the second-most expensive US hurricane on record—via different methods and show the significant role of the tweet retrieval source on study insights and results. One dataset was collected using keywords to filter relevant data, the other using geographical location. We find that while keyword-based data is more suited to tracking public engagement and identifying information brokers, location-based data is needed to characterize local situational information and communication behaviors. Meanwhile, both datasets have limitations. For example, keywords-based data may underestimate connections among users, while location-based data does not capture sentiment from a broad audience. Hence, in the spirit of advocating for increased transparency and reproducibility of data-driven research, we provide a set of data reporting guidelines based on our findings, to serve as the first step towards trustworthy crisis informatics.

With increased internet connectivity, there are more opportunities to substitute physical, or “in-person”, activities with online activities. With this, changes to household and individual travel/activity patterns are to be expected. For example, the increased adoption of e-commerce can affect individual travel destinations and frequency. To study these shifts, we enhance an existing smartphone-based travel/activity diary to capture online activities, with a focus on activities that can replace passenger trips. Furthermore, we expand the options to report shopping details, to better understand the influence of e-commerce on in-person shopping. In this paper, we detail the data collection tool development strategy and user-experience findings. Moreover, we provide insights from a pilot deployment in Singapore during 2020, which allowed measuring travel/activity behavior, expenditure, and time allocation changes during various COVID-19 pandemic travel restriction periods.

Background: The introduction of the electronic health record (EHR) has improved the collection and storage of patient information, enhancing clinical communication and academic research. However, EHRs are limited by data quality and the time-consuming task of manual data extraction. This study aimed to use process mapping to help identify critical data entry points within the clinical pathway for patients with vestibular schwannoma (VS) ideal for structured data entry and automated data collection to improve patient care and research., Methods: A 2-stage methodology was used at a neurosurgical unit. Process maps were developed using semi-structured interviews with stakeholders in the management of VS resection. Process maps were then retrospectively validated against EHRs for patients admitted between August 2019 and December 2021, establishing critical data entry points., Results: In the process map development, 20 stakeholders were interviewed. Process maps were validated against EHRs of 36 patients admitted for VS resection. Operative notes, surgical inpatient reviews (including ward rounds), and discharge summaries were available for all patients, representing critical data entry points. Areas for documentation improvement were in the preoperative clinics (30/36; 83.3%), preoperative skull base multidisciplinary team (32/36; 88.9%), postoperative follow-up clinics (32/36; 88.9%), and postoperative skull base multidisciplinary team meeting (29/36; 80.6%)., Conclusions: This is a first use to our knowledge of a 2-stage methodology for process mapping the clinical pathway for patients undergoing VS resection. We identified critical data entry points that can be targeted for structured data entry and for automated data collection tools, positively impacting patient care and research., (Crown Copyright © 2024. Published by Elsevier Inc. All rights reserved.)

Background: Designing questionnaires is a key point of epidemiological studies assessing human exposure to chemicals. The lack of validated questionnaires can lead to the use of previously developed and sub-optimally adapted questionnaires, which may result in information biases that affect the study’s validity. On this ground, a multidisciplinary group of researchers developed a series of tools to support data collection within the HBM4EU initiative. The objective of this paper is to share the process of developing HBM4EU questionnaires, as well as to provide researchers with harmonized procedures that could help them to design future questionnaires to assess environmental exposures. Methods: In the frame of the work package on survey design and fieldwork of the HBM4EU, researchers carried out procedures necessary for the development of quality questionnaires and related data collection tools. These procedures consisted of a systematic search to identify questionnaires used in previous human biomonitoring (HBM) studies, as well as the development of a checklist and evaluation sheet to assess the questionnaires identified. The results of these evaluations were taken into consideration for the development of the final questionnaires. Results: The main points covered by each of the sections included in HBM4EU questionnaires are described and discussed in detail. Additional tools developed for data collection in the HBM4EU (e.g. non-responder questionnaire, satisfaction questionnaire, matrix-specific questionnaire) are also addressed. Special attention is paid to the limitations faced and hurdles overcome during the process of questionnaire development. Conclusions: Designing questionnaires for use in HBM studies requires substantial effort by a multidisciplinary team to guarantee that the quality of the information collected meets the study’s objectives. The process of questionnaire development described herein will contribute to improve the harmonization of HBM studies within the social and environmental context of the EU countries.

Registries provide an important tool for data collection, particularly in context of rare conditions, or questions not amenable to investigation by means of a clinical trial. The Subcommittee on Women's Health in Thrombosis and Hemostasis (WHITH); one of 20 Scientific and Standardization Subcommittees (SSCs) of the International Society on Thrombosis and Hemostasis (ISTH) has supported 12 registries related to WHITH since 2016. Despite many successes, some challenges have been observed. In this report, we attempt to review the basic tenets of value in developing a registry, discuss observed challenges, share lessons learned, and finally present our views on a potential formula for success in developing and maintaining a registry in WHITH.

This article is part of a series of articles in the Journal of the Academy of Nutrition and Dietetics exploring the importance of research design, epidemiological methods, and statistical analysis as applied to nutrition and dietetics research. The purpose of this ongoing statistical portfolio is to assist registered dietitian nutritionists and nutrition and dietetic technicians, registered with interpreting nutrition research and applying scientific principles to produce high-quality data analysis. A survey is a systematic method for collecting reportable information on a topic of interest. Developing, adapting, and conducting survey research is a complex process; its aim is to collect accurate and useful data for the intended purpose and context. This article, which accompanies a companion article on electronic survey research, is an overview of survey methodology for data collection and analysis in nutrition and dietetics research. Its purpose is to highlight the general principles and components of survey development and survey administration that would maximize the validity of the data obtained. The goal is to provide a practical guide on the design and implementation of a survey as a method for data collection. Supporting figures are provided for use in direct application by practitioners and students., (Copyright © 2024 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.)

Background: Data collection by mailing questionnaires to the study population is one of the main research methods in epidemiologic studies. As participation rates are decreasing, easy-to-implement and cost-effective strategies to increase survey participation are needed. In this study, we tested the effect of a pragmatic combination of evidence-based interventions., Methods: We conducted a two-armed randomized controlled trial, nested in a cohort of breast cancer survivors (n = 1000) in the setting of a health outcomes survey. The intervention arm received a postal pre-notification, a non-monetary incentive (ballpoint with the study logo) and an alternative invitation letter in which several lay-out and textual adjustments were implemented according to behavioural science techniques. The alternative invitation letter also contained a QR-code through which an information video about the study could be accessed. The control arm was invited according to standard practice. Participants had the option to fill-out a questionnaire either on paper or online. A questionnaire with more than 50% of the questions answered classified as participation., Results: Overall participation rate was 62.9%. No significant difference in participation rate was observed between intervention and control arm (64.5% vs 61.3%, Risk Ratio (RR) 1.05, 95% CI [0.96 - 1.16]). Older age at study (>65 vs <51 years), and high socio-economic status (highest vs lowest quartile) were associated with higher participation rates (RR 1.30, 95% CI [1.07 - 1.57] and 1.24, 95% CI [1.09 - 1.42] respectively). In-situ carcinoma compared to invasive cancer and longer interval since treatment were associated with lower participation (RR 0.86, 95% CI [0.74 - 0.99] and RR 0.92, 95% CI [0.87 - 0.99] per 5 year increase, respectively)., Conclusion: Overall, the combination of four interventions tested in this study did not improve survey participation among breast cancer survivors. The overall participation rate was relatively high, possibly due to the study population of cancer survivors., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: F.E. van Leeuwen reports financial support was provided by Netherlands Ministry of Health Welfare and Sport. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: Touching Hands is an American Society for Surgery of the Hand program that provides hand surgeries to the world's underserved communities. The purpose of this study was to develop and implement a systematic data collection method for Touching Hands to assess patient outcomes, volunteer impact, alleviated disease burden, and cost-effectiveness., Methods: Research electronic data capture (REDCap) was used as the secure software platform to facilitate data collection. The Quick Disabilities of the Arm, Shoulder and Hand questionnaire was used to assess pre-and postoperative patient-reported outcomes. The Maslach Burnout Inventory-Human Services (Medical Personnel) survey was administered to volunteers before and after the mission to measure impact on volunteers. Case information was collected to calculate disability-adjusted life years and cost-effectiveness., Results: The data collection system was implemented in some capacity in 4 domestic and 3 international mission sites during 2020 and 2021., Conclusions: Substantial limitations exist for the implementation of a systematic data collection framework for Touching Hands and warrant further modification and optimization., Clinical Relevance: A REDCap database can be used for standardized and centralized patient and volunteer data collection for Touching Hands missions., (Copyright © 2024 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.)

Study region: Benin-Owena and Ogun Oshun river basins, Nigeria Study focus: Nigeria is a signatory to the United Nations 2030 Agenda and its Sustainable Development Goals (SDGs). Target 6.1 of Goal 6 has requested member nations to achieve universal and equitable access to safe and affordable drinking water for all by 2030. To meet this target as the country population reaches 257.8 million by 2030 in the face of limited freshwater resource will require collecting water resource, water use, socio-economic and environmental data in accordance with the provision of the River Basin Development Authority Act CAP 396 LFN 1990 (section 4(1)(e)). Implementing data collection at the river basin level will help inform and support action towards the realisation of Nigeria’s drinking water target. This study therefore explores the situation of data collection in Nigeria and exposes the factors constraining its full implementation. New hydrological insights: Evidence distilled from data gathering sources revealed that: (a) only hydrological and meteorological data were being collected by the River Basin Development Authorities (RBDAs), (b) even with that, inadequate financial resources constrained its full implementation, and (c) the inability of the RBDAs to collect hydrogeological, water use, socio-economic, and environmental data was traced to institutional factors. This study offers proposals on measures which might help to improve the situation towards a better data collection implementation at the river basin level in Nigeria.

In this study the authors compare two methods for airport asphalt pavement distress data collection applied on the main runway of Amílcar Cabral international airport, located at Sal Island in Cape Verde. The two methods used for testing were traditional visual inspection (on-foot) and an indirect method using a vehicle equipped with image capture and recording, lasers and geolocation devices (in-vehicle inspection). The aim of this research is to contribute to the validation of the proposed low-cost in-vehicle pavement distress inspection system with semiautomatic data processing in order to be considered in the implementation of the pavement condition assessment component of airport pavement management systems (APMS). This is a particularly important component as from the collected distress data it is possible to assess the condition of the pavements and define intervention strategies. Validation of the indirect data collection method is evaluated by statistical comparison of the collected distress data and pavement condition index (PCI) obtained from both methods. Statistically non-significant differences between the result sets validate the proposed indirect method, however the analysis evidenced two aspects that need improvement in the proposed system, namely the quality of the captured images to identify distresses with lower severity level and inspector training for proper allocation of severity levels during image analysis. This results in significant advantages considering that the total amount of the runway pavement area is inspected. Inspection time is reduced and data collection cost can be reduced. Processing and results visualization on GIS environment allows revaluation of the dataset on the in-vehicle method. Data interpretation and measurements quality control becomes simpler and faster.

Outdoor recreation is a known source of disturbance to many wildlife populations. We systematically reviewed 126 relevant papers that study the impact of outdoor recreation on wildlife, focusing on terrestrial wildlife (birds excluded) to assess the different methodological approaches adopted by researchers. We characterised the research methods into seven categories (direct observation, indirect observation (field-based), telemetry, camera traps, physiological measurement, trapping, and simulation). We find that direct observation is the most commonly used method to capture human-wildlife interactions, followed by the use of telemetry, and camera traps. The animals most commonly studied were ungulates, and the orders Carnivora and Rodentia. Studies typically captured data over longer periods (median 54 months) when using trapping methods; other methods exhibited shorter study durations (median 22 months). The size of the animal under study appears to influence how methods are chosen, with larger species often being studied using telemetry methods. We highlight advantages and disadvantages of each method depending on the aims of the study, the focal species, and the type of outdoor recreation. Our review highlights the need for simultaneous measurements of both human activity and wildlife response. We also recommend that researchers consider how to capture both short- and long-term impacts on animal welfare. Our findings should guide applied wildlife conservation and management research in scenarios where human-wildlife interactions lead to conservation issues.

Growing attention on the benefits of non-motorized travel has increased the demand for accurate and timely pedestrian and bicycle travel data. Advancements in technologies and the proliferation of smartphones have created new data sources that can help eliminate limitations related to small sample size and infrequent updates due to limited resources. This study reviews the emerging data sources and their current use, focusing on non-motorized travel monitoring. In this study, the emerging data are categorized into mode-unspecified and mode-specified data based on whether the mode used can be detected with no or little effort. While mode-unspecified data are collected without sorting out non-motorized travelers, mode-specified data at least know who (which mode) is being monitored. So far, commercial vendors provide a vast volume of mode-unspecified data, but their products have been mainly used for motorized trips or are in initial stages of development. Meanwhile, readily available data sources and their applications are more concentrated on mode-specified data, which have enabled varying non-motorized travel studies—including travel pattern identification, route-choice modeling, crash/air pollution exposure estimation, and new facility provision evaluation—but are mostly focused on bicycling. Despite the potential of emerging data, their use also has several challenges, such as limited mode inference, sample bias, and lack of detailed trip/traveler information due to privacy issues. More efforts are needed, such as improving data accuracy and developing robust data fusion techniques, to be able to fully utilize the emerging data sources.

Medical artificial intelligence (AI) is an important technical asset to support medical supply-side reforms and national development in the big data era. Clinical data from multiple disciplines represent building blocks for the development and application of AI-aided diagnostic and treatment systems based on medical big data. However, the inconsistent quality of these data resources in AI research leads to waste and inefficiencies. Therefore, it is crucial that the field formulates the requirements and content related to data processing as part of the development of intelligent medicine. To promote medical AI research worldwide, the “Belt and Road” International Ophthalmic Artificial Intelligence Research and Development Alliance will establish a series of expert recommendations for data quality in intelligent medicine.

Objectives: To determine whether Amazon's Mechanical Turk (MTurk) might be a viable means of recruiting participants for online intervention research. This was accomplished by conducting a randomized controlled trial of a previously validated intervention with participants recruited through MTurk. Methods: Participants were recruited to complete an online survey about their alcohol use through the MTurk platform. Those who met eligibility criterion for age and problem drinking were invited to complete a 3-month follow-up. Those who agreed were randomized to receive access to an online brief intervention for drinking or were assigned to a no intervention control group (i.e., thanked and told that they would be re-contacted in 3 months). Results: A total of 423 participants were recruited, of which 85% were followed-up at 3-months. All participants were recruited in 3.2 h. Only 1/3 of participants asked to access the online brief intervention did so. Of the 4 outcome variables (number of drinks in a typical week, highest number on one occasion, number of consequences, AUDIT consumption subscale), one displayed a significant difference between conditions. Participants in the intervention group reported a greater reduction between on the AUDIT consumption subscale between baseline and 3-month follow-up compared to those in the no intervention control group (p = 0.004). Conclusions: Despite the current pilot showing only limited evidence of impact of the intervention among participants recruited through MTurk, there is potential for conducting trials employing this population (particularly if methods are employed to make sure that participants receive the intervention). This potential is important as it could allow for the rapid conduct of multiple trials during the development stages of online interventions.

Without data, there is no new knowledge generated. There may be interesting speculation, new paradigms or theories, but without data gathered from the universe, as representative of the truth in the universe as possible, there will be no new knowledge. Therefore, it is important to become excellent at collecting, collating and correctly interpreting data. Pre-existing and new data sources are discussed; variables are discussed, and sampling methods are covered. The importance of a detailed protocol and research manual are emphasized. Data collectors and data collection forms, both electronic and paper-based are discussed. Ensuring subject privacy while also ensuring appropriate data retention must be balanced.

Objective: This article aims to estimate the differences in environmental impact (greenhouse gas [GHG] emissions, land use, energy used, acidification and potential eutrophication) after one year of promoting a Mediterranean diet (MD)., Methods: Baseline and 1-year follow-up data from 5800 participants in the PREDIMED-Plus study were used. Each participant's food intake was estimated using validated semi-quantitative food frequency questionnaires, and the adherence to MD using the Dietary Score. The influence of diet on environmental impact was assessed through the EAT-Lancet Commission tables. The influence of diet on environmental impact was assessed through the EAT-Lancet Commission tables. The association between MD adherence and its environmental impact was calculated using adjusted multivariate linear regression models., Results: After one year of intervention, the kcal/day consumed was significantly reduced (-125,1 kcal/day), adherence to a MD pattern was improved (+0,9) and the environmental impact due to the diet was significantly reduced (GHG: -361 g/CO2-eq; Acidification:-11,5 g SO2-eq; Eutrophication:-4,7 g PO4-eq; Energy use:-842,7 kJ; and Land use:-2,2 m2). Higher adherence to MD (high vs. low) was significantly associated with lower environmental impact both at baseline and one year follow-up. Meat products had the greatest environmental impact in all the factors analysed, both at baseline and at one-year follow-up, in spite of the reduction observed in their consumption., Conclusions: A program promoting a MD, after one year of intervention, significantly reduced the environmental impact in all the factors analysed. Meat products had the greatest environmental impact in all the dimensions analysed., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: To assess the relationship between the self-perceived consultation time and the knowledge of the proposed treatment., Methods: Secondary data from a 2015 national survey of health services in Peru were analyzed. The self-perceived consultation time was calculated by asking how long it took from when you entered the consultation until you departed. It was then categorized as low, medium, and high. Five self-reported questions were used to construct a knowledge of the prescribed treatment. Adjusted regression models from the Poisson family models were used to evaluate the relationship. We report adjusted prevalence ratios (aPR) with their 95% confidence intervals (95%CI)., Results: A total of 9939 outpatients were analyzed, with 58% women; the average age was 44 years; and 45.4% had higher education. Using low self-perceived consultation time as references, medium and high consultation times were associated with understanding the prescribed treatment (aPR=1.17; 95%CI, 1.04-1.33 and aPR=1.30; 95%CI, 1.20-1.40, respectively)., Conclusion: Patients who reported a medium and high self-perceived consultation time better understood the prescribed treatment., Implications for Practice: Healthcare professionals should strive to maximize consultation time to ensure effective communication and improve patient knowledge of treatments, improving overall patient satisfaction and health outcomes., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare that are relevant to the content of this article., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In recent years, the number of horses submitted for necropsy has been in decline. This study aimed to identify the perceptions and barriers of equine necropsy through an online survey of horse owners. The data were analyzed by multivariate models for associations and factors that influenced decision making regarding necropsy. The survey yielded 1,366 usable responses. Respondents had higher familiarity with the term "autopsy" compared to "necropsy" (P < 0.001) and showed a greater level of comfort using the terms "body" and "mortality" compared to "carcass" (P < 0.001). Most respondents (82 %) had experienced euthanizing a horse, but few (27 %) were offered a necropsy. Of those offered a necropsy, a majority (81 %) opted to perform one. When asked about their reasons for choosing to have a necropsy performed, recommendations from veterinarians (77.87 OR, 95 % CI: 49.3, 127.5) and already knowing the cause of death (1.85 OR, 95 % CI: 1.2, 30) were positively associated while a benefit to herd health was negatively associated (0.55 OR, 95 % CI: 0.3, 1.0). Owning horses for use as competition animals (1.44 OR, 95 % CI: 0.9, 2.2), the desire for personal closure (1.76 OR, 95 % CI: 1.1, 2.9), and the goals to protect human health (1.29 OR, 95 % CI: 1.0, 1.6) and to improve herd health (1.43 OR, 95 % CI: 1.1, 1.9) were positively associated with a likelihood of choosing to have a necropsy performed or recommending necropsies in the future. This research highlights the need for education of horse owners about the option and benefits of necropsy, and the use of more societally accepted language., Competing Interests: Declaration of competing interest None of the authors has any financial or personal relationships that could influence or bias the content of the paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

CropLife Europe collected literature values from monitoring studies measuring air concentrations of Plant Protection Products (PPPs) that may be inhaled by humans located in rural areas but not immediately adjacent to PPP applications. The resulting "Combined Air Concentration Database" (CACD) was used to determine whether air concentrations of PPPs reported by the French "Agency for Food, Environmental and Occupational Health & Safety" (ANSES) are consistent with those measured by others to increase confidence in values of exposure to humans. The results were put into risk assessment context. Results show that 25-90% of samples do not contain measurable PPP concentrations. Measured respirable fractions were below EU default air concentrations used for risk assessment for resident exposure by the European Food Safety Authority. All measured exposures in the CACD were also below established toxicological endpoints, even when considering the highest maximum average reported concentrations and very conservative inhalation rates. The highest recorded air concentration was for prosulfocarb (0.696 μg/m³ measured over 48 h) which is below the EFSA default limit of 1 μg/m³ for low volatility substances. In conclusion, based on the CACD, measured air concentrations of PPPs are significantly lower than EFSA default limits and relevant toxicological reference values., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Anne-Kim Vinck, Edgars Felkers, Michel Urtizberea, Kathrin Bürling and Alistair Morriss are employees of companies that conduct and evaluate risk assessments for regulatory purposes in the context of authorization and marketing of their companies' products. They contribute as scientific experts to the industry association CropLife Europe for evaluation and development of the state-of-the-art methodology. Nicola Hewitt is a scientific consultant., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims: Amongst elderly people with type 2 diabetes (T2D) over prescribing can result in emergency ambulance call-outs, falls and fractures and increased mortality, particularly in frail patients. Current clinical guidelines, however, remain focused on medication intensification rather than deintensification where appropriate. This study aims to evaluate the effectiveness of an electronic decision-support system and training for the deintensification of potentially inappropriate medications amongst older frail people with T2D, when compared to 'usual' care at 12-months., Methods: This study is an open-label, multi-site, two-armed pragmatic cluster-randomised trial. GP practices randomised to the 'enhanced care' group have an electronic decision support system installed and receive training on the tool and de-intensification of diabetes medications. The system flags eligible patients for possible deintensification of diabetes medications, linking the health care professional to a clinical algorithm. The primary outcome will be the number of patients at 12-months who have had potentially inappropriate diabetes medications de-intensified., Results: Study recruitment commenced in June 2022. Data collection commenced in January 2023. Baseline data have been extracted from 40 practices (3145 patients)., Conclusions: Digital technology, involving computer decision systems, may have the potential to reduce inappropriate medications and aid the process of de-intensification., Trial Registration: International Standard Randomised Controlled Trial Number: ISRCTN53221378. Available at: https://www.isrctn.com/ISRCTN53221378., Competing Interests: Declaration of Competing Interest KK has acted as a consultant and speaker for Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, and Merck Sharp & Dohme. He has received grants in support of investigator and investigator-initiated trials from Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim, and Merck Sharp & Dohme. SS has received funds for research, consultancy to or honoraria from Boehringer Ingelheim, Lilly, Merck Sharp & Dohme Limited, Novartis Pharmaceuticals UK Ltd, Novo Nordisk Limited, Sanofi-Aventis, Amgen, Astra Zeneca, Abbott Janssen-Cilag Ltd, NAPP, and Takeda UK Ltd. JPS receives funding from the Wellcome Trust/Royal Society via a Sir Henry Dale Fellowship (ref: 211182/Z/18/Z), the National Institute for Health and Care Research (NIHR) and from the British Heart Foundation (refs: PG/21/10341; FS/19/13/34235). This research was funded in part, by the Wellcome Trust [211182/Z/18/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. All other authors: none declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)