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Ethical considerations, including transparency, play an important role when using artificial intelligence (AI) in education. Explainable AI has been coined as a solution to provide more insight into the inner workings of AI algorithms. However, carefully designed user studies on how to design explanations for AI in education are still limited. The current study aimed to identify the effect of explanations of an automated essay scoring system on students' trust and motivation. The explanations were designed using a needs-elicitation study with students in combination with guidelines and frameworks of explainable AI. Two types of explanations were tested: full-text global explanations and an accuracy statement. The results showed that both explanations did not have an effect on student trust or motivation compared to no explanations. Interestingly, the grade provided by the system, and especially the difference between the student's self-estimated grade and the system grade, showed a large influence. Hence, it is important to consider the effects of the outcome of the system (here: grade) when considering the effect of explanations of AI in education.

Teaching algorithmic thinking enables students to use their knowledge in various contexts to reuse existing solutions to algorithmic problems. The aim of this study is to examine how students recognize which algorithmic concepts can be used in a new situation. We developed a card sorting task and investigated the ways in which secondary school students arranged algorithmic problems (Bebras tasks) into groups using algorithm as a criterion. Furthermore, we examined the students' explanations for their groupings. The results of this qualitative study indicate that students may recognize underlying algorithmic concepts directly or by identifying similarities with a previously solved problem; however, the direct recognition was more successful. Our findings also include the factors that play a role in students' recognition of algorithmic concepts, such as the degree of similarity to problems discussed during lessons. Our study highlights the significance of teaching students how to recognize the structure of algorithmic problems.

Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and running the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical challenges.

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for dropout prevention explicit. Data of a Dutch vocational education institute is used to show how out-of-sample machine learning predictions can be used to formulate invitation rules in a way that targets students at risk more effectively, thereby facilitating early detection for effective dropout prevention.

Background and Context: Computing education is expanding, while the teaching of algorithms is less well studied. Objective: The aim of this study was to examine teachers' pedagogical content knowledge (PCK) for teaching algorithms. Method: We conducted semi-structured interviews with seven computer science (CS) teachers in upper secondary education (students aged 15-18). The data were analyzed qualitatively. Findings: We found two patterns of variation in teachers' PCK. First, we detected variation in the teachers' goals related to their view of algorithms: they either focused on "thinking" about the algorithm as an object, or focused on "thinking and making", where the algorithm is also regarded as a program. Second, we found variation in teachers' knowledge about responding to differences between students, which may be generic or topic-specific. Furthermore, our findings reveal that teachers consider class discussions to play a significant role as an instructional method for provoking reflection. Implications: Our findings regarding PCK may be beneficial for the development of teacher education and professionalization activities for CS teachers.

Whereas 'Web 2.0 technology' has pushed the learning agenda towards connectivity and boundary crossing, in the current 'new new media ontology' the fear that algorithms might block our avenues to knowledge and connections prevails. In response to this, media scholars have argued that knowledge based on the algorithmic experiences of users is key to reformulating the agenda of critical media education. In this study of the algorithmic experiences of secondary education students in the Netherlands, we want to contribute to building such knowledge, making use of the concepts of algorithmic imagination, power and critical evaluation. Results show students build situational, practical-experiential knowledge of algorithmic workings that is closely in line with the features of the interface of the social media platforms they use. Implications for media literacy include providing students with system-level awareness and agency, including insights into the societal-political consequences of algorithmic workings.

Studied the use of adaptive testing when examinees are classified into three categories. Established testing algorithms with two different statistical computation procedures and evaluated them through simulation using an operative item bank from Dutch basic adult education. Results suggest a reduction of at least 22% in the mean number of items for a computerized adaptive test. (SLD)

Presents thinking-aloud protocols from secondary pupils solving arithmetic story and geometrical problems and analyzes their protocols by using interpretation models founded on a general problem-solving model. A word problem solving simulation is also used to further explicate processes pupils use to solve mathematical tasks. (Author/MBR)

In a large-scale field experiment aimed at improving spelling instruction in Dutch schools, the effectiveness of a newly developed algorithmic teaching method was compared with the conventionally and commonly used analogy approach. The analogy group showed comparatively little progress in learning results. (Seventy-five test items are appended). (Author/LMO)

Characterizes typical training programs on "learning to think," outlines past research, and lists criteria for an aptitude-treatment interaction (ATI) study of the relationship between tasks, training methods, and student attributes. Details are given of two computerized experiments examining the different effects of algorithmic and heuristic approaches on problem solving. (EAO)

Simple Summary: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers, and most patients present with advanced or irresectable disease due to late recognition. Radiological imaging modalities such as CT scans are key in providing information on the presence or absence of tumors. However, an assessment of pancreatic cancer requires specific radiological expertise, and small tumors are easily overlooked. Computer-aided detection (CAD) using artificial intelligence (AI) techniques is promising and may help in the early detection of pancreatic tumors. In this study, we developed a deep learning-based tumor detection framework that can detect pancreatic head cancer on CT scans with high accuracy when incorporating clinically relevant information. We demonstrate that a tumor detection framework utilizing CT scans and secondary signs of pancreatic tumors results in an increased detection accuracy for the detection of pancreatic head tumors. The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy. [ABSTRACT FROM AUTHOR]

Older adults (OA) after mild traumatic brain injury (mTBI) have a high risk of developing persistent post-injury cognitive impairments. Lower pre-morbid cognitive reserve (CR) is increasingly investigated as a risk factor for cognitive dysfunction in OA. However, how CR protects against effects of mTBI at the brain level remains largely understudied. We examined 22 OA who sustained mTBI (mean 67.69 years, SD 5.11) in the sub-acute phase and 15 age- and CR-matched healthy OA (mean 68 years, SD 5.55) performing a three-level visual N-back task using electroencephalography. We calculated inverse efficiency scores of performance from accuracy and reaction times. Event-related potentials served as neurocognitive correlates of attentional (P2) and working memory (P3) processing. Overall, mTBI OA performed worse than healthy OA (p = 0.031). Lower CR generally decreased performance (p < 0.001). Furthermore, with increasing task difficulty, task performance was more affected by CR (p = 0.004). At the brain level, P2 amplitude was lower in mTBI OA than in healthy OA (p = 0.05). There was no clear effect of CR on P2 or P3 measures. As mTBI OA with lower CR performed worse on a working-memory task, lower CR may be a risk factor for worse recovery after mTBI in this group. [ABSTRACT FROM AUTHOR]

Background: Optimizing transitional care by practicing family-centered care might reduce unplanned events for patients who undergo major abdominal cancer surgery. However, it remains unknown whether involving family caregivers in patients' healthcare also has negative consequences for patient safety. This study assessed the safety of family involvement in patients' healthcare by examining the cause of unplanned events in patients who participated in a family involvement program (FIP) after major abdominal cancer surgery. Methods: This is a secondary analysis focusing on the intervention group of a prospective cohort study conducted in the Netherlands. Data were collected from April 2019 to May 2022. Participants in the intervention group were patients who engaged in a FIP. Unplanned events were analyzed, and root causes were identified using the medical version of a prevention- and recovery-information system for monitoring and analysis (PRISMA) that analyses unintended events in healthcare. Unplanned events were compared between patients who received care from family caregivers and patients who received professional at-home care after discharge. A Mann-Whitney U test was used to analyze data. Results: Of the 152 FIP participants, 68 experienced an unplanned event and were included. 112 unplanned events occurred with 145 root causes since some unplanned events had several root causes. Most root causes of unplanned events were patient-related factors (n = 109, 75%), such as patient characteristics and disease-related factors. No root causes due to inadequate healthcare from the family caregiver were identified. Unplanned events did not differ statistically (interquartile range 1–2) (p = 0.35) between patients who received care from trained family caregivers and those who received professional at-home care after discharge. Conclusion: Based on the insights from the root-cause analysis in this prospective multicenter study, it appears that unplanned emergency room visits and hospital readmissions are not related to the active involvement of family caregivers in surgical follow-up care. Moreover, surgical follow-up care by trained family caregivers during hospitalization was not associated with increased rates of unplanned adverse events. Hence, the concept of active family involvement by proficiently trained family caregivers in postoperative care appears safe and feasible for patients undergoing major abdominal surgery. [ABSTRACT FROM AUTHOR]

Background: A mallet finger (MF) is diagnosed clinically and can be managed in primary care. The actual incidence of MF and how it is managed in primary care is unknown. Aim: To determine the incidence of MF in primary care and to obtain estimates for the proportions of osseous and tendon MF. An additional aim was to gain insight into the management of patients diagnosed with MF in primary care. Design & setting: A cohort study using a healthcare registration database from general practice in the Netherlands. Method: Patients aged =18 years with a new diagnosis of MF from 1 January 2015-31 December 2019 were selected using a search algorithm based on International Classification of Primary Care (ICPC) coding. Results: In total, 161 cases of MF were identified. The mean incidence was 0.58 per 1000 person-years. A radiograph was taken in 58% (n = 93) of cases; 23% (n = 37) of cases had an osseous MF. The most applied strategies were referral to secondary care (45%) or conservative treatment in GP practice (43%). Overall, 7% were referred to a paramedical professional. Conclusion: On average, a Dutch GP assesses =1 patient with MF per year. Since only a minimal number of patients required surgical treatment and a limited number of GPs requested radiography, the recommendation in the guidelines to perform radiography in all patients with MF should potentially be reconsidered. The purpose of requesting radiographs should not be to distinguish between a tendinogenic or osseous MF, but to assess whether there is a possible indication for surgery. [ABSTRACT FROM AUTHOR]

An increasing number of media organisations are adopting news recommender systems (NRS). Such algorithmic technologies, which prioritise content based on, for example, previous user behaviour or popularity metrics, may have far-reaching repercussions for news work. Despite this, the implications of NRS implementation for intra-organisational practices as well as dynamics and tensions between involved actors remain understudied. Against this background, this study examines decision-making processes and relationships between actors participating in NRS projects from an institutional logics perspective and places a particular emphasis on resulting tensions between journalistic, market, and tech logics. Drawing on 32 in-depth qualitative interviews with news media professionals across ten news organisations in the Netherlands and Switzerland, we discover a wide range of strategies which aim to reconcile logic multiplicity in the specific case of NRS development. Such negotiation efforts can ultimately promote new work practices and forms of collaboration but may also have broader implications for the distribution of power and voice within news organisations. [ABSTRACT FROM AUTHOR]

The rise in health care costs, caused by older and more complex patient populations, requires Population Health Management approaches including risk stratification. With risk stratification, patients are assigned individual risk scores based on medical records. These patient stratifications focus on future high costs and expensive care utilization such as hospitalization, for which different models exist. With this study, the research team validated the accuracy of risk prediction scores for future hospitalization and high health care costs, calculated by the Adjusted Clinical Group (ACG)'s risk stratification models, using Dutch primary health care data registries. In addition, they aimed to adjust the US-based predictive models for Dutch primary care. The statistical validity of the existing models was assessed. In addition, the underlying prediction models were trained on 95,262 patients' data from de Zoetermeer region and externally validated on data of 48,780 patients from Zeist, Nijkerk, and Urk. Information on age, sex, number of general practitioner visits, International Classification of Primary Care coded information on the diagnosis and Anatomical Therapeutic Chemical Classification coded information on the prescribed medications, were incorporated in the model. C-statistics were used to validate the discriminatory ability of the models. Calibrating ability was assessed by visual inspection of calibration plots. Adjustment of the hospitalization model based on Dutch data improved C-statistics from 0.69 to 0.75, whereas adjustment of the high-cost model improved C-statistics from 0.78 to 0.85, indicating good discrimination of the models. The models also showed good calibration. In conclusion, the local adjustments of the ACG prediction models show great potential for use in Dutch primary care. [ABSTRACT FROM AUTHOR]

As algorithms play an increasingly important role in public organizations, we see a rise in the number of public sector data scientists. Even though the relevance and risks of algorithms in the public sector are broadly discussed, our current academic knowledge of public sector data scientists and their work is limited. To develop an understanding of their work practices, data scientists have been studied in two Dutch government organizations. In a core period of 5 months per organization, I conducted in-depth qualitative research into the work of the data scientists, their role in the organization, and their relationship with other actors at two regulatory agencies in the Netherlands. The analysis shows that data scientists integrate Silicon Valley and engineering, domain, as well as political–administrative logics in their work practices. Thus, the work of the data scientists is hybrid. However, even though the organizational contexts are very similar, hybrid work takes very different forms both across organizations and over time. This dynamic hybridity is linked to different algorithmization processes and outcomes in the two organizations. The results suggest that hybridity in public sector data scientists' work should be adapted to organizational and technological aspects of transformation processes and aspired outcomes. [ABSTRACT FROM AUTHOR]

Through the introduction of algorithmic systems into police organizations, a new employee emerged: the data professional. Contrary to street-level officers, little is known of the discretionary power of these system-level bureaucrats. Our qualitative research into the Netherlands Police provides a first empirical and theoretical understanding. The study shows that data professionals exert discretion and are aware of public values, but their value-sensitivity often does not translate into responsible practices. Data professionals use a variety of arguments to dissociate themselves from, or downplay, their responsibilities. We conclude that this distancing hampers the connection between the discretion and responsibility of data professionals. [ABSTRACT FROM AUTHOR]

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95% CI = 0.77-0.9) for the test cohort (n = 639), which could be increased to AUROC = 0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI = 90–100%]; HPV testing: five-year survival rate: 80% [95% CI = 71–90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR = 0.15 [95% CI = 0.05–0.44], intermediate HR = 0.58 [95% CI = 0.34–0.98] p = 0.043, Cox proportional hazards model, n = 211; HPV testing: HR = 0.29 [95% CI = 0.15–0.54] p < 0.001, Cox proportional hazards model, n = 211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Background: The Dutch guideline algorithm for the analysis of anaemia in patients of general practitioners (GPs) was programmed in a Clinical Decision Support system (CDS-anaemia) to support the process of diagnosing the cause of anaemia in the laboratory. This research aims to assess the supplementary benefit provided by the automated algorithm in various demographic categories, including different sexes, age groups and severities of anaemia, in comparison to the manual diagnostic approach employed by GPs., Methods: This was a retrospective cohort study of 5399 primary care patients where the cause of anaemia was diagnosed by GPs with or without the aid of CDS-anaemia within the age groups 18-44, 45-64, 65-79 and 80 and older. Anaemia was defined according to the Dutch College of General Practitioners (DCGP) guideline. Causes of anaemia were based on the DCGP guidelines with the corresponding blood tests. By calculation of rate ratios and percentage differences of the determined cause of anaemia we evaluated the effect of the diagnostic algorithm., Results and Conclusion: The percentage patients in which an underlying cause of anaemia was found increased 34 and 46 percentage points in females and males, respectively, when GPs were supported by CDS-anaemia compared to GPs who were not supported by CDS-anaemia. The highest increase in percentage points when CDS-anaemia was used, was found in younger- and middle-aged males and mild or moderate anaemia., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides., Methods: The algorithm was developed using digitalized whole-slide images obtained in a single-centre (Erasmus MC Cancer Institute, the Netherlands) cohort of patients who underwent first curative intent resection for colorectal liver metastases between January 2000 and February 2019. External validation was performed on whole-slide images of patients resected between October 2004 and December 2017 in another institution (Radboud University Medical Center, the Netherlands). The outcomes of interest were the automated classification of dichotomous hepatic growth patterns, distinguishing between desmoplastic hepatic growth pattern and non-desmoplatic growth pattern by a deep-learning model; secondary outcome was the correlation of these classifications with overall survival in the histopathology manual-assessed histopathological growth pattern and those assessed using neural image compression., Results: Nine hundred and thirty-two patients, corresponding to 3.641 whole-slide images, were reviewed to develop the algorithm and 870 whole-slide images were used for external validation. Median follow-up for the development and the validation cohorts was 43 and 29 months respectively. The neural image compression approach achieved significant discriminatory power to classify 100% desmoplastic histopathological growth pattern with an area under the curve of 0.93 in the development cohort and 0.95 upon external validation. Both the histopathology manual-scored histopathological growth pattern and neural image compression-classified histopathological growth pattern achieved a similar multivariable hazard ratio for desmoplastic versus non-desmoplastic growth pattern in the development cohort (histopathology manual score: 0.63 versus neural image compression: 0.64) and in the validation cohort (histopathology manual score: 0.40 versus neural image compression: 0.48)., Conclusions: The neural image compression approach is suitable for pathology-based classification tasks of colorectal liver metastases., (© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.)

Introduction: Telephone triage is pivotal for evaluating the urgency of patient care, and in the Netherlands, the Netherlands Triage Standard (NTS) demonstrates moderate discrimination for chest pain. To address this, the Safety First Prediction Rule (SFPR) was developed to improve the safety of ruling out acute coronary syndrome (ACS) during telephone triage., Methods: We conducted an external validation of the SFPR using data from the TRACE study, a retrospective cohort study in out-of-hours primary care. We evaluated the diagnostic accuracy assessment for ACS, major adverse cardiovascular events (MACE), and major events within 6 weeks. Moreover, we compared its performance with that of the NTS algorithm., Results: Among 1404 included patients (57.3% female, 6.8% ACS, 8.6% MACE), the SFPR demonstrated good discrimination for ACS (C-statistic: 0.79; 95%-CI: 0.75-0.83) and MACE (C-statistic: 0.79; 95%-CI: 0.0.76-0.82). Calibration was satisfactory, with overestimation observed in high-risk patients for ACS. The SFPR (risk threshold 2.5%) trended toward higher sensitivity (95.8% vs. 86.3%) and negative predictive value (99.3% vs. 97.6%) with a lower negative likelihood ratio (0.10 vs. 0.34) than the NTS algorithm., Conclusion: The SFPR proved robust for risk stratification in patients with acute chest pain seeking out-of-hours primary care in the Netherlands. Further prospective validation and implementation are warranted to refine and establish the rule's clinical utility., (© The Author(s) 2024. Published by Oxford University Press.)

Background: The World Falls Guidelines (WFG) propose an algorithm that classifies patients as low-, intermediate-, and high-risk. We evaluated different operationalizations of the WFG algorithm and compared its predictive performance to other screening tools for falls, namely: the American Geriatrics Society and British Geriatrics Society (AGS/BGS) algorithm, the 3KQ on their own and fall history on its own., Methods: We included data from 1509 adults aged ≥65 years from the population-based Longitudinal Aging Study Amsterdam. The outcome was ≥1 fall during 1-year follow-up, which was ascertained using fall calendars. The screening tools' items were retrospectively operationalized using baseline measures, using proxies where necessary., Results: Sensitivity ranged between 30.9-48.0% and specificity ranged between 77.0-88.2%. Operationalizing the algorithm with the 3KQ instead of fall history yielded a higher sensitivity but lower specificity, whereas operationalization with the Clinical Frailty Scale (CFS) classification tree instead of Fried's frailty criteria did not affect predictive performance. Compared to the WFG algorithm, the AGS/BGS algorithm and fall history on its own yielded similar predictive performance, whereas the 3KQ on their own yielded a higher sensitivity but lower specificity., Conclusion: The WFG algorithm can identify patients at risk of a fall, especially when the 3KQ are included in its operationalization. The CFS and Fried's frailty criteria may be used interchangeably in the algorithm's operationalization. The algorithm performed similarly compared to other screening tools, except for the 3KQ on their own, which have higher sensitivity but lower specificity and lack clinical recommendations per risk category., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)

Objective: Observational studies using electronic health record (EHR) databases often face challenges due to unspecific clinical codes that can obscure detailed medical information, hindering precise data analysis. In this study, we aimed to assess the feasibility of refining these unspecific condition codes into more specific codes in a Dutch general practitioner (GP) EHR database by leveraging the available clinical free text., Methods: We utilized three approaches for text classification-search queries, semi-supervised learning, and supervised learning-to improve the specificity of ten unspecific International Classification of Primary Care (ICPC-1) codes. Two text representations and three machine learning algorithms were evaluated for the (semi-)supervised models. Additionally, we measured the improvement achieved by the refinement process on all code occurrences in the database., Results: The classification models performed well for most codes. In general, no single classification approach consistently outperformed the others. However, there were variations in the relative performance of the classification approaches within each code and in the use of different text representations and machine learning algorithms. Class imbalance and limited training data affected the performance of the (semi-)supervised models, yet the simple search queries remained particularly effective. Ultimately, the developed models improved the specificity of over half of all the unspecific code occurrences in the database., Conclusions: Our findings show the feasibility of using information from clinical text to improve the specificity of unspecific condition codes in observational healthcare databases, even with a limited range of machine-learning techniques and modest annotated training sets. Future work could investigate transfer learning, integration of structured data, alternative semi-supervised methods, and validation of models across healthcare settings. The improved level of detail enriches the interpretation of medical information and can benefit observational research and patient care., Competing Interests: Declaration of competing interest The authors 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 The Author(s). Published by Elsevier B.V. All rights reserved.)

Anterior and posterior drooling are prevalent comorbidities in children with neurodevelopmental disabilities. Considering the heterogeneity of the patient population and the multifactorial aetiology of drooling, an interdisciplinary and individualised treatment approach is indispensable. However, no tool for stepwise decision-making in the treatment of paediatric drooling has been developed previously. Within the Radboudumc Amalia Children's Hospital, care for children with anterior and/or posterior drooling secondary to neurodevelopmental disabilities is coordinated by a saliva control team with healthcare professionals from six disciplines. In alignment with international literature, published guidelines, and evidence gained from two decades of experience and research by our team, this paper proposes an algorithm reflecting the assessment and treatment approach applied in our clinic. First, directions are provided to decide on the necessity of saliva control treatment, taking type of drooling, the child's age, and the severity and impact of drooling into account. Second, the algorithm offers guidance on the choice between available treatment options, highlighting the importance of accounting for child characteristics and child and caregiver preferences in clinical (shared) decision-making., Conclusions: With this algorithm, we aim to emphasise the importance of repeated stepwise decision-making in the assessment and treatment of drooling in children during their childhood, encouraging healthcare professionals to apply a holistic approach., What Is Known: • Children with anterior or posterior drooling secondary to neurodevelopmental disabilities comprise a heterogeneous group, necessitating an individualised treatment approach. • No stepwise decision-making tool is available for the treatment of paediatric drooling., What Is New: • Deciding on the necessity of saliva control treatment should be a conscious process, based on type of drooling, age, and drooling severity and impact. • Type of drooling, age, cognition, oral motor skills, self-awareness, posture, diagnosis, and child/caregiver preferences need to be considered to decide on the optimal treatment., (© 2024. The Author(s).)

The paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM) called fGAAM (fast GAAM) that significantly decreases the time needed to find feature subsets of a satisfactory classification accuracy. To demonstrate the time gains provided by fGAAM both algorithms were tested on eight datasets containing different number of features, classes, and examples. The fGAAM was also compared with four reference methods: the Holland GA with and without penalty term, Culling GA, and NSGA II. Results: (i) The fGAAM processing time was about 35% shorter than that of the original GAAM. (ii) The fGAAM was also 20 times quicker than two Holland GAs and 50 times quicker than NSGA II. (iii) For datasets of different number of features, classes, and examples, another number of individuals, stored for further processing, provided the highest acceleration. On average, the best results were obtained when individuals from the last 10 populations were stored (time acceleration: 36.39%) or when the number of individuals to be stored was calculated by the algorithm itself (time acceleration: 35.74%). (iv) The fGAAM was able to process all datasets used in the study, even those that, because of their high number of features, could not be processed by the two Holland GAs and NSGA II. [ABSTRACT FROM AUTHOR]

Background: Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results. Methods: Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes. Results: The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased. Conclusion: SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients. [ABSTRACT FROM AUTHOR]

During the last decade, rainfall monitoring using signal-level data from commercial microwave links (CMLs) in cellular communication networks has been proposed as a complementary way to estimate rainfall for large areas. Path-averaged rainfall is retrieved between the transmitting and receiving cellular antennas of a CML. One rainfall estimation algorithm for CMLs is RAINLINK, which has been employed in different regions (e.g., Brazil, Italy, the Netherlands, and Pakistan) with satisfactory results. However, the RAINLINK parameters have been calibrated for a unique optimum solution, which is inconsistent with the fact that multiple similar or equivalent solutions may exist due to uncertainties in algorithm structure, input data, and parameters. Here, we show how CML rainfall estimates can be improved by calibrating all parameters of the algorithm systematically and simultaneously with the stochastic particle swarm optimization method, which is used for the numerical maximization of the objective function. An open dataset of approximately 2800 sub-links of minimum and maximum received signal levels over 15 min intervals covering the Netherlands (∼ 35 500 km 2) is employed: 12 d are used for calibration and 3 months for validation. A gauge-adjusted radar rainfall dataset is utilized as a reference. Verification of path-average daily rainfall shows a reasonable improvement for the stochastically calibrated parameters with respect to RAINLINK's default parameter settings. Results further improve when averaged over the Netherlands. Moreover, the method provides a better underpinning of the chosen parameter values and is therefore of general interest for calibration of RAINLINK's parameters for other climates and cellular communication networks. [ABSTRACT FROM AUTHOR]

Channels in rivers and estuaries are the main paths of fluvial and tidal currents that transport sediment through the system. While network representations of multi‐channel systems and their connectivity are quite useful for characterisation of braiding patterns and dynamics, the recognition of channels and their properties is complicated because of the large bed elevation variations, such as shallow shoals and bed steps that render channels visually disconnected. We present and analyse two mathematically rigorous methods to identify channel networks from a terrain model of the river bed. Both methods construct a dense network of locally steepest‐descent channels from saddle points on the terrain, and select a subset of channels with a certain minimum sediment volume between them. This is closely linked to the main mechanism of channel formation and change by displacement of sediment volume. The two methods differ in how they compute these sediment volumes: either globally through the entire length of the river, or locally. We compare the methods for the measured bathymetry of the Western Scheldt estuary, The Netherlands, over the past decades. The global method is overly sensitive to small changes elsewhere in the network compared to the local method. We conclude that the local method works best conceptually and for stability reasons. The associated concept of alluvial connectivity between channels in a network is thus the inverse of the volume of sediment that must be displaced to merge the channels. Our method opens up possibilities for new analyses as shown in two examples. First, it shows a clear pattern of scale dependence on volume of the total network length and of the number of nodes by a power law relation, showing that the smaller channels are relatively much shorter. Second, channel bifurcations were found to be predominantly mildly asymmetrical, which is unexpected from fluvial bifurcation theory. [ABSTRACT FROM AUTHOR]

Background & Aims: Cancer is one of the great human challenges in all countries, both advanced and developing. Cancer treatment management can include surgery, chemotherapy, or radiation therapy (1). Radiation therapy is done in two ways: Teletherapy and Brachytherapy. Brachytherapy involves the use of radiation sources to treat cancer by irradiating cancerous tissue from within the patient’s body (2). But the dose and how to use this method has always been questionable for researchers. Therefore, this study creates a new fuzzy approach to high-dose brachytherapy by optimizing the distribution of double roughness based on dosimetric criteria. The use of fuzzy logic itself has increased the accuracy of the mathematical model of the problem. This fuzzy model is a new study and innovation used in this paper. Due to the fuzzy nature of this method and its limitations, it is considered fuzzy. This makes the method more accurate and includes parameters such as the patient’s physical ability and age in the problem, which in itself increases the accuracy of the method for each patient. As a result, the obtained answer is improved and the executive program of brachytherapy method is more accurate. Methods: In the present study, the dose prescribed for an organ was evaluated by dosimetric indices listed in Table 1 (18). For the present study, data from 20 patients in the age range of 50 to 74 and mean age 62 years with a wide range of prostate volume between 23 and 103 cubic centimeters, and for the treatment of prostate cancer by brachytherapy from the Academic Medical Center (AMC, Amsterdam, the Netherlands) had participated. To compare brachytherapy programs with high interstitial dose, the dose rate was calculated with 192Ir beam with a radiation dose of 13 Gy, according to the standard protocol TG-43. To begin with, computed tomography (CT) scans or magnetic resonance imaging (MRI) were taken from the patients pelvis, and entered into the treatment planning software for use in treatment planning sessions. BT treatment planners and specialists then determined the input catheters, target volumes, and OARs obtained from the medical images. Depending on the size and exact location of the target volumes, between 14 and 20 catheters entered the patient’s body, reaching the target volumes. After designing and approving an acceptable treatment plan, the catheters inserted into the patient’s body were connected to a retractor 4 that controls the movement of the radiation source. After the treatment program, the source was returned to the retractor (8, 14). Then, an integer program model with fuzzy constraints was proposed for programming on high-dose brachytherapy. The description of infrastructures, parameters and variables in this model are in Table 2 (15 ). Finally, the dosimeter index is equal to the sum of all the index variables, as it turned out: The proposed model is a correct programming model called IP. In general, three evolutionary algorithms (EP), (GA) and (ES) were used in this research. The algorithms stopped after creating 20 generations of desirable answers and the best answer of each generation was determined on the chart as a point. The resulting set of answers were connected in the form of graphs, which are used to analyze the results. Each point on the graph identifies the best answer from each of the generations generated by the respective algorithms. These three algorithms were performed independently for each patient and the obtained answers were identified as dots on the chart. The graph obtained for each patient indicates the capability of each of these algorithms. Results: According to this study, each of the three algorithms (EP), (GA) and (ES) are each run independently for each of the patients with prostate cancer. At first, the genetic algorithm showed the ability to get closer to the desired answer sooner, but as the optimization process continues, the rate of convergence to the desired answer decreases, so if the time parameter is very important, the genetic algorithm can be useful. Especially for patients whose prostate volume was larger than other patients. In patients 15, 12, 8 and 9, due to their younger age than other patients and better physical condition, as well as prostate volume less than 80 ml, they had much more promising results than other patients. In contrast, for patients 4 and 19 with an age of over 70, the results were not as favorable as for other patients. The * sign in the table indicates that there is no answer through that patient-specific algorithm that covers 95 or even more of the tumor volume. Therefore, according to Table 2, it can be concluded that the best algorithm that can be considered for the case where the shortest time to reach the target coverage above 95% is the genetic algorithm. According to Table 2, it can be concluded that the best algorithm that can be considered for the case where the shortest time to reach the target coverage above 95% is the genetic algorithm. Therefore, according to Table 3, the ES algorithm has better answers than the other two algorithms for the case in which the largest volume of the tumor is covered. According to the results, the ES algorithm has obtained the best results for patients under 60 years of age and normal prostate volume. Conclusion: According to the obtained results, it can be stated that whether the main goal is the maximum coverage or the goal is the shortest possible time to reach the coverage above 95%, the best algorithm that can get a good answer for each patient is the evolutionary strategy algorithm. [ABSTRACT FROM AUTHOR]

Introduction: The course of OCD differs widely among OCD patients, varying from chronic symptoms to full remission. No tools for individual prediction of OCD remission are currently available. This study aimed to develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily accessible in the daily clinical routine.Methods: Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision trees were used as supervised machine learning technique. The training of the algorithm was performed with 227 predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm was subsequently tested in an independent sample of 215 observations collected in five different centers. Between-center differences were investigated with a bootstrap resampling approach.Results: The average predictive performance of the algorithm in the test centers resulted in an AUROC of 0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD chronic course, use of psychotropic medications, and better global functioning.Limitations: All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover, the sample of the data recruited in some of the test centers was limited in size.Discussion: The algorithm demonstrated a moderate average predictive performance, and future studies will focus on increasing the stability of the predictive performance across clinical settings. [ABSTRACT FROM AUTHOR]

Background Diagnosing urinary tract infections (UTIs) in nursing home residents is complex, as specific urinary symptoms are often absent and asymptomatic bacteriuria (ASB) is prevalent. The aim of this study was to assess the sensitivity of blood C-reactive protein (CRP) and procalcitonin (PCT), measured by point-of-care tests (PoCTs), to diagnose UTIs in this setting. Methods Elderly residents (≥65 years old) with a suspected UTI were recruited from psychogeriatric, somatic, or rehabilitation wards across 13 participating nursing homes. CRP and PCT were tested simultaneously in the same study participants. To assess the tests' sensitivities, a stringent definition of "true" UTI was used that included the presence of symptoms, urinary leucocytes, a positive urine culture, and symptom resolution during antibiotic treatment covering isolated uropathogen(s). The original sample size was 440 suspected UTI episodes, in order to detect a clinically relevant sensitivity of at least 65% when calculated using the matched analysis approach to compare both PoCTs. Results After enrollment of 302 episodes (68.6% of the planned sample size), an unplanned and funder-mandated interim analysis was done, resulting in premature discontinuation of the study for futility. For 247 of 266 eligible episodes, all mandatory items required for the true UTI definition (92.9%) were available. In total, 49 episodes fulfilled our stringent UTI definition (19.8%). The sensitivities of CRP (cut-off, 6.5 mg/L) and PCT (cut-off, 0.025 ng/mL) were 52.3% (95% confidence interval [CI], 36.7–67.5%) and 37.0% (95% CI, 23.2–52.5%), respectively. Conclusions Our results indicate that CRP and PCT are not suitable tests for distinguishing UTI and ASB in nursing home residents. Clinical Trials Registration Netherlands Trial Registry NL6293. [ABSTRACT FROM AUTHOR]

Decision-making in healthcare often relies on narrative guidelines; however, these instruments are poorly accessible for supporting clinical decision-making. This study explores the application of rule-based decision logic in algorithmic modeling, emphasizing its great potential in clinical decision support and research. Integrating rule-based algorithms with existing information systems and real-world data poses a serious challenge. Integrating decision algorithms with information standards increases their effectiveness across various applications. This study outlines a method for constructing clinical decision trees (CDTs), highlighting their transparency and interpretability, using information standards as a design principle. We use the digitization of the Dutch breast cancer guideline through CDTs as a case study to exemplify their versatility and practical significance. The process step 'primary treatment' has been successfully translated from the narrative guidelines format to the anticipated ted computational format.

Objectives: Volunteer responder systems (VRSs) aim to decrease time to defibrillation by dispatching trained volunteers to automated external defibrillators (AEDs) and out-of-hospital cardiac arrest (OHCA) victims. AEDs are often underutilized due to poor placement. This study provides a cost-effectiveness analysis of adding AEDs at strategic locations to maximize quality-adjusted life years (QALYs)., Methods: We simulated combined volunteer, police, firefighter, and emergency medical service response scenarios to OHCAs, and applied our methods to a case study of Amsterdam, the Netherlands. We compared the competing strategies of placing additional AEDs, using steps of 40 extra AEDs (0, 40, …, 1480), in addition to the existing 369 AEDs. Incremental cost-effectiveness ratios (ICERs) were calculated for each increase in additional AEDs, from a societal perspective. The effect of AED connection and time to connection on survival to hospital admission and neurological outcome at discharge was estimated using logistic regression, using OHCA data from Amsterdam from 2006 to 2018. Other model inputs were obtained from literature., Results: Purchasing up to 1120 additional AEDs (ICER €75,669/QALY) was cost-effective at a willingness-to-pay threshold of €80,000/QALY, when positioned strategically. Compared to current practice, adding 1120 AEDs resulted in a gain of 0.111 QALYs (95% CI 0.110-0.112) at an increased cost of €3792 per OHCA (95% CI €3778-€3807). Health benefits per AED diminished as more AEDs were added., Conclusions: Our study identified cost-effective strategies to position AEDs at strategic locations in a VRS. The case study findings advocate for a substantial increase in the number of AEDs in Amsterdam., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hans van Schuppen reports grants from Stryker Emergency Care and Zoll Foundation to his own institution, outside the scope of this study. All other authors reported no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner., Methods: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms., Results: We successfully applied our ETL tool and observed a complete coverage of the cohorts' data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data., Conclusion: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses., Competing Interests: Declaration of competing interest The authors 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 The Author(s). Published by Elsevier Inc. All rights reserved.)

The objective of this observational study was to compare 4 cow-level algorithms to predict cow-level intramammary infection (IMI) status (culture and MALDI-TOF) in late-lactation US dairy cows using standard measures of test performance. Secondary objectives were to estimate the likely effect of each algorithm, if used to guide selective dry cow therapy (SDCT), on dry cow antibiotic use in US dairy herds, and to investigate the importance of including clinical mastitis criteria in algorithm-guided SDCT. Cows (n = 1,594) from 56 US dairy herds were recruited as part of a previously published cross-sectional study of bedding management and IMI in late-lactation cows. Each herd was visited twice for sampling. At each farm visit, aseptic quarter-milk samples were collected from 20 cows approaching dry-off (>180 d pregnant), which were cultured using standard bacteriological methods and MALDI-TOF for identification of isolates. Quarter-level culture results were used to establish cow-level IMI status, which was considered the reference test in this study. Clinical mastitis records and Dairy Herd Improvement Association test-day somatic cell count data were extracted from herd records and used to perform cow-level risk assessments (low vs. high risk) using 4 algorithms that have been proposed for SDCT in New Zealand, the Netherlands, United Kingdom, and the United States. Agreement between aerobic culture (reference test; IMI vs. no-IMI) and algorithm status (high vs. low risk) was described using Cohen's kappa, test sensitivity, specificity, negative predictive value, and positive predictive value. The proportion of cows classified as high risk among the 4 algorithms ranged from 0.31 to 0.63, indicating that these approaches to SDCT could reduce antibiotic use at dry-off by 37 to 69% in the average US herd. All algorithms had poor agreement with IMI status, with kappa values ranging from 0.05 to 0.13. Sensitivity varied by pathogen, with higher values observed when detecting IMI caused by Streptococcus uberis, Streptococcus dysgalactiae, Staphylococcus aureus, and Lactococcus lactis. Negative predictive values were high for major pathogens among all algorithms (≥0.87), which may explain why algorithm-guided SDCT programs have been successfully implemented in field trials, despite poor agreement with overall IMI status. Removal of clinical mastitis criteria for each algorithm had little effect on the algorithm classification of cows, indicating that algorithms based on SCC alone may have similar performance to those based on SCC and clinical mastitis criteria. We recommend that producers implementing algorithm-guided SDCT use algorithm criteria that matches their relative aspirations for reducing antibiotic use (high specificity, positive predictive value) or minimizing untreated IMI at dry-off (high sensitivity, negative predictive value). [ABSTRACT FROM AUTHOR]

Algorithms are widely used in our data-driven media landscape. Many misconceptions have arisen about how these algorithms work and what they can do. In this study, we conducted a large representative survey (N = 2,106) in the Netherlands to explore algorithmic misconceptions. Results showed that a significant part of the general population holds (multiple) misconceptions about algorithms in the media. We found that erroneous beliefs about algorithms are more common among (1) older people (vs. younger people), (2) lower-educated people (vs. higher-educated), and (3) women (vs. men). In addition, it was found that people who had no specific sources to inform themselves about algorithms, and those relying on their friends/family for information, were more likely to have algorithmic misconceptions. Conversely, media channels, school, and having one's own (online) experiences were found to be sources associated with having fewer algorithmic misconceptions. Theoretical implications are formulated in the context of algorithmic awareness and the digital divide. Finally, societal implications are discussed, such as the need for algorithmic literacy initiatives. [ABSTRACT FROM AUTHOR]

Objective: In view of global ageing and the scarcity of knowledge about disease determinants in older individuals with rheumatoid arthritis (RA), an algorithm with optimal diagnostic accuracy was developed to identify RA patients in the Longitudinal Ageing Study Amsterdam (LASA). Method: Four case ascertainment algorithms were constructed and assessed for validity in LASA, an ongoing cohort study (≥ 55 years) representing the general older population of the Netherlands. Data sources used to identify the diagnosis RA were: self-reported morbidity, specialist diagnosis, and medication. A validation subsample of LASA participants was taken to verify RA diagnosis by a standard procedure using a checklist. Results: Data from 272/300 (91%) participants were verified. Four algorithms were developed: 'treatment', 'diagnosis', 'treatment or diagnosis', and 'treatment and diagnosis'. The algorithm 'treatment and diagnosis' showed the best measurement properties: specificity 100%, positive predictive value 100%, and area under the receiver operating characteristics curve 0.72. Applying this algorithm in the LASA sample (mean age 71 years) revealed a prevalence of RA of 1.0% (19/1908 participants). Conclusion: An algorithm for RA identification in the LASA population was developed, with high diagnostic accuracy. It provides an accurate tool to identify older adults with RA in LASA and, after validation, may be applicable in other large population-based studies. [ABSTRACT FROM AUTHOR]

The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week. [ABSTRACT FROM AUTHOR]

Background: Trauma-related (preventable) death is used to evaluate the management and quality of trauma care worldwide. Therefore, it is necessary to identify fatalities in the trauma care population and assess them on preventability. However, the definition on trauma-related preventable death lacks validity due to differences in terminology and classifications. This study aims to reach consensus on the definition of trauma-related preventable death by performing a Delphi procedure, thereby, improving the assessment of trauma-related preventable death and thereby enhancing the quality of trauma care., Methods: Based on the results of a recently performed systematic review Hakkenbrak (2021). The definitions used to describe trauma-related preventable death could be divided into four categories: 1) Clinical definition based on panel review or expert opinion, 2) Trauma prediction algorithm, 3) Clinical definition with an additional trauma prediction algorithm and 4) Others (e.g., errors in care or detailed clinical definition). A three round, electronic Delphi study will be performed in the Netherlands to reach consensus. Experts from the department of Trauma surgery, Neurosurgery, Forensic medicine, Anaesthesiology and Emergency medicine, of the designated Level 1 trauma centres in the Netherlands, will be invited to participate. In the first round the panel will comment on the composed categories and trauma prediction algorithms. In the second and third round a feedback report will be presented and the questions with disagreement will be retested., Discussion: The identification and assessment of trauma-related preventable death is necessary to evaluate and improve trauma care. Therefore, a valid, fair, and applicable definition of trauma-related preventable death is required. The Delphi technique is utilized to reach group consensus to obtain a scientifically valid definition of trauma-related preventable death., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hakkenbrak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: Adult-onset Still's disease (AOSD) is a rare condition characterized by fevers, rash, and arthralgia/arthritis; most doctors treating AOSD in the Netherlands treat <5 patients per year. Currently, there is no internationally accepted treatment guideline for AOSD. The objectives of this study were to conduct a Delphi panel aimed at reaching consensus about diagnostic and treatment strategies for patients with AOSD and to use the outcomes as a basis for a treatment algorithm., Methods: The Delphi panel brought together 18 AOSD experts: rheumatologists, internists and paediatricians. The Delphi process consisted of three rounds. In the first two rounds, online lists of questions and statements were completed. In the third round, final statements were discussed during a virtual meeting and a final vote took place. Consensus threshold was set at 80%. Two targeted literature searches were performed identifying the level of evidence of the consensus-based statements., Results: Consensus was reached on 29 statements, including statements related to diagnosis and diagnostic tests, definition of response and remission, the therapy, the use of methotrexate and tapering of treatment. The panel consented on reduction of the use of glucocorticoids to avoid side effects, and preferred the use of biologics over conventional treatment. The role of IL-1 and IL-6 blocking agents was considered important in the treatment of AOSD., Conclusion: In this Delphi panel, a high level of consensus was achieved on recommendations for diagnosis and therapy of AOSD that can serve as a basis for a treatment guideline., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Background: Persons with multimorbidity may gain from person-centred care compared with the current protocolised chronic-disease management in Dutch general practice. Given time constraints and limited resources, it is essential to prioritise those most in need of an assessment of person-centred chronic-care needs., Aim: To develop and validate a prioritisation algorithm based on routine electronic medical record (EMR) data that distinguishes between patients with multimorbidity who would, and those who would not, benefit from an extended person-centred consultation to assess person-centred chronic-care needs, as judged by GPs., Design and Setting: A mixed-methods study was conducted in five general practices in the north-west region of the Netherlands. Four out of the five practices were situated in rural areas., Method: Multivariable logistic regression using EMR data to predict the GPs' judgement on patients' anticipated benefit from an extended consultation, as well as a thematic analysis of a focus group exploring GPs' clinical reasoning for this judgement were conducted. Internal validation was performed using 10-fold cross-validation. Multimorbidity was defined as the presence of ≥3 chronic conditions., Results: In total, EMRs from 1032 patients were included in the analysis; of these, 352 (34.1%) were judged to have anticipated benefit. The model's cross-validated C-statistic was 0.72 (95% confidence interval = 0.70 to 0.75). Calibration was good. Presence of home visit(s) and history of myocardial infarction were associated with anticipated benefit. Thematic analysis revealed three dimensions feeding anticipated benefit: GPs' cause for concern, patients' mindset regarding their conditions, and balance between received care/expected care needed., Conclusion: This algorithm may facilitate automated prioritisation, potentially avoiding the need for GPs to personally triage the whole practice population that has multimorbidity. However, external validation of the algorithm and evaluation of actual benefit of consultation is recommended before implementation., (© The Authors.)