Hasil untuk "Computer applications to medicine. Medical informatics"

S. McLean

Introduction Case-based learning (CBL) is a newer modality of teaching healthcare. In order to evaluate how CBL is currently used, a literature search and review was completed. Methods A literature search was completed using an OVID© database using PubMed as the data source, 1946-8/1/2015. Key words used were “Case-based learning” and “medical education”, and 360 articles were retrieved. Of these, 70 articles were selected to review for location, human health care related fields of study, number of students, topics, delivery methods, and student level. Results All major continents had studies on CBL. Education levels were 64% undergraduate and 34% graduate. Medicine was the most frequently represented field, with articles on nursing, occupational therapy, allied health, child development and dentistry. Mean number of students per study was 214 (7–3105). The top 3 most common methods of delivery were live presentation in 49%, followed by computer or web-based in 20% followed by mixed modalities in 19%. The top 3 outcome evaluations were: survey of participants, knowledge test, and test plus survey, with practice outcomes less frequent. Selected studies were reviewed in greater detail, highlighting advantages and disadvantages of CBL, comparisons to Problem-based learning, variety of fields in healthcare, variety in student experience, curriculum implementation, and finally impact on patient care. Conclusions CBL is a teaching tool used in a variety of medical fields using human cases to impart relevance and aid in connecting theory to practice. The impact of CBL can reach from simple knowledge gains to changing patient care outcomes.

Nimrod Serok, Shlomo Havlin, Efrat Blumenfeld Lieberthal

Abstract We introduce a novel decentralised traffic light control strategy, termed the Tree Method, designed to mitigate the challenges posed by conflicting traffic flows operating on competing cycle times during specific phases at traffic intersections. This methodology hinges on the precise identification and subsequent prioritisation of congestion bottlenecks, assessed through their expansive influence on the entire road network. The Tree Method calculates the cost associated with each congestion tree and advances a prioritisation scheme that emphasises the global, rather than local, impact of traffic flow. To evaluate the effectiveness of this approach, we utilised the Simulation of Urban Mobility (SUMO) to conduct a series of simulations incorporating both realistic and abstract Origin-Destination (OD) matrices across varying traffic conditions. The Tree Method demonstrated a significant capability in identifying the principal contributors to congestion and their upstream effects, leading to major improvements in throughput and average travel times. Comparative analysis of the Tree Method against other, traffic light control techniques revealed superior performance also in improving conditions for the majority of drivers and across time. This means that traffic moves more smoothly through junctions, with fewer delays and shorter queues, even under heavy demand. Additionally, the simplicity of the Tree Method’s analytical framework supports real-time operational adjustments, aligning well with the dynamic feedback loops inherent in traffic flow systems. As cities face growing congestion challenges, our findings highlight a control strategy that is both effective and simple enough to be deployed in real urban environments.

Zerubabel Desita, MD, Temesgen Tadesse, MD, Anders Solitander Bohlbro, MD et al.

Objective: To assess whether computer-aided detection (CAD) chest X-ray (CXR) software may aid physicians in low-resource, high tuberculosis (TB) endemic settings where radiologists are scarce. Patients and Methods: A retrospective pilot study was conducted on CXR films taken between January 1, 2017, and March 30, 2018, in Guinea-Bissau and Ethiopia to compare the interpretation of CXRs regarding pulmonary TB (PTB) by CAD (qXR; Qure.ai) with that of 2 experienced Ethiopian radiologists (A and B). To improve the applicability of this method in low-resource settings, an analysis was performed on images of CXRs taken by mobile phones. Two reference standards were applied: final PTB diagnosis by clinical or laboratory findings (ie, Xpert MTB/RIF [Xpert]-confirmed PTB). Results: We included 498 CXRs from patients seeking help for TB indicative symptoms. Radiologist A identified 50, radiologist B identified 99, and the software identified 81 as indicative of TB. The overall area under the curve for the receiver-operating characteristic curve of the software was 0.84 for Xpert-confirmed cases. At the prechosen cutoff value of 0.5, the sensitivity of CAD CXR was 76.5%, and the specificity was 85.9%. Radiologist A’s assessments were 64.7% sensitive and 91.9% specific, whereas radiologist B’s assessments were 76.5% sensitive and 82.3% specific for Xpert-confirmed cases. The agreement regarding TB-related findings between the radiologists combined (κ=0.45) and each radiologist and the software (κ=0.56) was moderate. Conclusion: Our study revealed that CAD CXR performs comparably with experienced radiologists when it is applied to CXR films, photographed by mobile phones and a digital camera with similar sensor resolutions. Trial registration: PACTR201611001838365.

Adam Mustapha, Ahmed Nouri Alsharksi, Abdalla Ali Salim et al.

The increasing prevalence of CTX-M β-lactamase-producing Klebsiella pneumoniae has become a critical public health concern due to its ability to hydrolyse broad-spectrum β-lactam antibiotics, thereby undermining clinical treatment. This study applied a multistage computational analysis to identify and characterise potential inhibitors of CTX-M β-lactamase. Virtual screening of the ZINC database compounds using RASPD+ yielded 21,246 candidates, which were refined through physicochemical, pharmacokinetic, and medicinal chemistry filters. Twenty compounds met drug-likeness and ADMET criteria and were subjected to molecular docking using AutoDock 4.2. The top hits, ZINC12152876 (−9.54 kcal/mol) and ZINC15305595 (-9.65 kcal/mol), exhibited stronger binding affinities than Avibactam (-6.20 kcal/mol) through hydrogen bonding with Ser73, Ser240, and Asn135 and hydrophobic stabilisation within the Ω-loop. Three-hundred-nanosecond molecular dynamics simulations in Amber22 confirmed the high structural stability of both complexes, with ZINC12152876 displaying superior compactness, low RMSD, and stable hydrogen-bond networks. MM-GBSA analysis revealed total binding energies of -44.4 and -43.5 kcal/mol for ZINC12152876 and ZINC15305595, respectively, dominated by van der Waals and electrostatic interactions. DFT-based HOMO-LUMO and molecular electrostatic potential analyses showed that ZINC12152876 possessed higher electronic softness and stronger polar character, supporting its strong interaction profile. The combined results highlight ZINC12152876 as a promising lead compound for the rational design of potent non-covalent CTX-M β-lactamase inhibitors against multidrug-resistant K. pneumoniae.

Saleh Alrashed, Nasro Min-Allah

Eliott Lumet, Thomas Jaravel, Mélanie C. Rochoux

By 2050, two-thirds of the world's population will live in urban areas under climate change, exacerbating the environmental and public health risks associated with poor air quality and urban heat island effects. Assessing these risks requires the development of microscale meteorological models that quickly and accurately predict wind velocity and pollutant concentration with high resolution, as the heterogeneity of urban environments leads to complex wind patterns and strong pollutant concentration gradients. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to address this challenge by providing obstacle-resolved flow and dispersion predictions. However, CFD models are very expensive and require intensive computing resources, which can hinder their systematic use in practical engineering applications. They are also subject to significant uncertainties, particularly those arising from the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, some of which are aleatory and thereby irreducible. Given these issues, the construction of CFD datasets that account for uncertainty would be an interesting avenue of research for microscale atmospheric science.In this context, we present the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which consists of 200 large-eddy simulations (LES) characterizing the complex interactions between the turbulent airflow, the tracer dispersion, and an idealized urban environment. These simulations reproduce the canonical MUST dispersion field campaign while perturbing the model's mesoscale meteorological forcing parameters. PPMLES includes time series at human height within the built environment to track wind velocity and pollutant release and dispersion over time. PPMLES also includes complete 3-D fields of first- and second-order temporal statistics of the wind velocity and pollutant concentration, with a sub-metric resolution. The uncertainty of the fields induced by the internal variability of the atmospheric boundary layer is also provided. The computation of PPMLES required significant resources, consuming 6 million CPU core hours, equivalent to the emission of approximately 10 tCO2eq of greenhouse gases. This significant computational effort and associated carbon footprint motivates the sharing of the data generated.The added value of the PPMLES dataset is twofold. First, the perturbed-parameter ensemble of LES enables to quantify and understand the effects of the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, which has been identified as a major challenge in predicting atmospheric flow and pollutant dispersion in urban environments. Secondly, PPMLES reference data can be used to benchmark models of different levels of complexity, and to extract key information about the physical processes involved to inform more operational modeling approaches, for example through learning surrogate models.

Yongqi Zheng, Meng Zhang, Conghui Wang et al.

Abstract BackgroundThe adoption of common data models (CDMs) has transformed pharmacoepidemiologic research by enabling standardized data formatting and shared analytical tools across institutions. These models facilitate large-scale, multicenter studies and support timely real-world evidence generation. However, no comprehensive global evaluation of CDM applications in pharmacoepidemiology has been conducted. ObjectiveThis study aimed to conduct a systematic review and bibliometric analysis to map the landscape of CDM usage in pharmacoepidemiology, including publication trends, institutional authors and collaborations, and citation impacts. MethodsIn total, 5 English databases (PubMed, Web of Science, Embase, Scopus, and Virtual Health Library) and 4 Chinese databases (CNKI, Wan-Fang Data, VIP, and SinoMed) were searched for studies applying CDMs in pharmacoepidemiology from database inception to January 2024. Two reviewers independently screened studies and extracted information about basic publication details, methodological details, and exposure and outcome information. The studies were categorized into 2 groups according to their Total Citations per Year (TCpY), and a comparative analysis was conducted to examine the differences in characteristics between the 2 groups. ResultsA total of 308 studies published between 1997 and 2024 were included, involving 1580 authors across 32 countries and 140 journals. The United States led in both publication volume and citation counts, followed by South Korea. Among the 10 most cited studies, 7 used the Vaccine Safety Datalink, 2 used Sentinel, and one used Observational Medical Outcomes Partnership. Studies were stratified by TCpY to reduce citation bias from publication timing. Comparative analysis showed that high-TCpY studies were significantly more associated with multicenter collaboration (PPP ConclusionsThis study presents the first bibliometric overview of CDM-based pharmacoepidemiologic research. The consistent output from United States institutions and increasing engagement from South Korea underscore their central roles in this field. High-TCpY studies tend to be multicenter, collaborative, and vaccine-focused, reflecting structural factors linked to research visibility and influence. Stratified citation analysis supports the value of real-world data integration and international cooperation in producing impactful studies. The dominance of limited-income countries in collaboration networks highlights a need for broader inclusion of underrepresented regions. These findings can help researchers identify key contributors, guide partner selection, and target appropriate journals. As CDM-based methods continue to expand, fostering diverse and collaborative research efforts will be crucial for advancing pharmacoepidemiologic knowledge globally.

Rosanne L van den Berg, Sophie M van der Landen, Matthijs J Keijzer et al.

BackgroundAssessment of cognitive decline in the earliest stages of Alzheimer disease (AD) is important but challenging. AD is a neurodegenerative disease characterized by gradual cognitive decline. Disease stages range from preclinical AD, in which individuals are cognitively unimpaired, to mild cognitive impairment (MCI) and dementia. Digital technologies promise to enable detection of early, subtle cognitive changes. Although the field of digital cognitive biomarkers is rapidly evolving, a comprehensive overview of the reporting of psychometric properties (ie, validity, reliability, responsiveness, and clinical meaningfulness) is missing. Insight into the extent to which these properties are evaluated is needed to identify the validation steps toward implementation. ObjectiveThis scoping review aimed to identify the reporting on quality characteristics of smartphone- and tablet-based cognitive tools with potential for remote administration in individuals with preclinical AD or MCI. We focused on both psychometric properties and practical tool characteristics. MethodsThis scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. In total, 4 databases (PubMed, Embase, Web of Science, and PsycINFO) were systematically searched from January 1, 2008, to January 5, 2023. Studies were included that assessed the psychometric properties of cognitive smartphone- or tablet-based tools with potential for remote administration in individuals with preclinical AD or MCI. In total, 2 reviewers independently screened titles and abstracts in ASReview, a screening tool that combines manual and automatic screening using an active learning algorithm. Thereafter, we manually screened full texts in the web application Rayyan. For each included study, 2 reviewers independently explored the reported information on practical and psychometric properties. For each psychometric property, examples were provided narratively. ResultsIn total, 11,300 deduplicated studies were identified in the search. After screening, 50 studies describing 37 different digital tools were included in this review. Average administration time was 13.8 (SD 10.1; range 1-32) minutes, but for 38% (14/37) of the tools, this was not described. Most tools (31/37, 84%) were examined in 1 language. The investigated populations were mainly individuals with MCI (34/37, 92%), and fewer tools were examined in individuals with preclinical AD (8/37, 22%). For almost all tools (36/37, 97%), construct validity was assessed through evaluation of clinical or biological associations or relevant group differences. For a small number of tools, information on structural validity (3/37, 8%), test-retest reliability (12/37, 32%), responsiveness (6/37, 16%), or clinical meaningfulness (0%) was reported. ConclusionsNumerous smartphone- and tablet-based tools to assess cognition in early AD are being developed, whereas studies concerning their psychometric properties are limited. Often, initial validation steps have been taken, yet further validation and careful selection of psychometrically valid outcome scores are required to demonstrate clinical usefulness with regard to the context of use, which is essential for implementation.

Fan Mao, Tianqi Huang, Longfei Ma et al.

During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.

Min K Chong, Ian B Hickie, Antonia Ottavio et al.

BackgroundLong wait times for mental health treatments may cause delays in early detection and management of suicidal ideation and behaviors, which are crucial for effective mental health care and suicide prevention. The use of digital technology is a potential solution for prompt identification of youth with high suicidality. ObjectiveThe primary aim of this study was to evaluate the use of a digital suicidality notification system designed to detect and respond to suicidal needs in youth mental health services. Second, the study aimed to characterize young people at different levels of suicidal ideation and behaviors. MethodsYoung people aged between 16 and 25 years completed multidimensional assessments using a digital platform, collecting demographic, clinical, social, functional, and suicidality information. When the suicidality score exceeded a predetermined threshold, established based on clinical expertise and service policies, a rule-based algorithm configured within the platform immediately generated an alert for treating clinicians. Subsequent clinical actions and response times were analyzed. ResultsA total of 2021 individuals participated, of whom 266 (11%) triggered one or more high suicidal ideation and behaviors notification. Of the 292 notifications generated, 76% (222/292) were resolved, with a median response time of 1.9 (range 0-50.8) days. Clinical actions initiated to address suicidality included creating safety plans (60%, 134/222), conducting safety checks (18%, 39/222), psychological therapy (8%, 17/222), transfer to another service (3%, 8/222), and scheduling of new appointments (2%, 4/222). Young people with high levels of suicidality were more likely to present with more severe and comorbid symptoms, including low engagement in work or education, heterogenous psychopathology, substance misuse, and recurrent illness. ConclusionsThe digital suicidality notification system facilitated prompt clinical actions by alerting clinicians to high levels of suicidal ideation and behaviors detected among youth. Further, the multidimensional assessment revealed complex and comorbid symptoms exhibited in youth with high suicidality. By expediting and personalizing care for those displaying elevated suicidality, the digital notification system can play a pivotal role in preventing rapid symptom progression and its detrimental impacts on young people’s mental health.

Cristian Soto Jacome, MD, Danny Segura Torres, MD, Jungwei W. Fan, PhD et al.

Objective: To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS). Patients and Methods: Between 2017 and 2021, we identified 18,000 TUS patients at Mayo Clinic and selected 628 for chart review to create a ground truth dataset based on consensus. We developed a rule-based NLP pipeline to identify TUS as appropriate TUS (aTUS) or inappropriate TUS (iTUS) using patients’ clinical notes and additional meta information. In addition, we designed an abbreviated NLP pipeline (aNLP) solely focusing on labels from TUS order requisitions to facilitate deployment at other health care systems. Our dataset was split into a training set of 468 (75%) and a test set of 160 (25%), using the former for rule development and the latter for performance evaluation. Results: There were 449 (95.9%) patients identified as aTUS and 19 (4.06%) as iTUS in the training set; there are 155 (96.88%) patients identified as aTUS and 5 (3.12%) were iTUS in the test set. In the training set, the pipeline achieved a sensitivity of 0.99, specificity of 0.95, and positive predictive value of 1.0 for detecting aTUS. The testing cohort revealed a sensitivity of 0.96, specificity of 0.80, and positive predictive value of 0.99. Similar performance metrics were observed in the aNLP pipeline. Conclusion: The NLP models can accurately identify the appropriateness of a thyroid ultrasound from clinical documentation and order requisition information, a critical initial step toward evaluating the drivers and outcomes of TUS use and subsequent thyroid cancer overdiagnosis.

Sammeli Liikkanen, Janne Sinkkonen, Joni Suorsa et al.

In the quantification of symptoms of Parkinson's disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed. 42 PD patients and 23 control subjects were monitored with Garmin Vivosmart 4 wearable device and asked to fill a symptom and medication diary with a mobile application, at home, for about four weeks. Subsequent analyses are based on continuous accelerometer data from the device. Accelerometer data from the Levodopa Response Study (MJFFd) were reanalyzed, with symptoms quantified with linear spectral models trained on expert evaluations present in the data. Variational autoencoders (VAE) were trained on both our study accelerometer data and on MJFFd to detect movement states (e.g., walking, standing). A total of 7590 self-reported symptoms were recorded during the study. 88.9% (32/36) of PD patients, 80.0% (4/5) of DBS PD patients and 95.5% (21/22) of control subjects reported that using the wearable device was very easy or easy. Recording a symptom at the time of the event was assessed as very easy or easy by 70.1% (29/41) of subjects with PD. Aggregated spectrograms of the collected accelerometer data show relative attenuation of low (<5Hz) frequencies in patients. Similar spectral patterns also separate symptom periods from immediately adjacent non-symptomatic periods. Discriminative power of linear models to separate symptoms from adjacent periods is weak, but aggregates show partial separability of patients vs. controls. The analysis reveals differential symptom detectability across movement tasks, motivating the third part of the study. VAEs trained on either dataset produced embedding from which movement states in MJFFd could be predicted. A VAE model was able to detect the movement states. Thus, a pre-detection of these states with a VAE from accelerometer data with good S/N ratio, and subsequent quantification of PD symptoms is a feasible strategy. The usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Finally, the usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients.

S. Zenker, D. Strech, K. Ihrig et al.

BACKGROUND The secondary use of deidentified but not anonymized patient data is a promising approach for enabling precision medicine and learning health care systems. In most national jurisdictions (e.g., in Europe), this type of secondary use requires patient consent. While various ethical, legal, and technical analyses have stressed the opportunities and challenges for different types of consent over the past decade, no country has yet established a national consent standard accepted by the relevant authorities. METHODS A working group of the national Medical Informatics Initiative in Germany conducted a requirements analysis and developed a GDPR-compliant broad consent standard. The development included consensus procedures within the Medical Informatics Initiative, a documented consultation process with all relevant stakeholder groups and authorities, and the ultimate submission for approval via the national data protection authorities. RESULTS This paper presents the broad consent text together with a guidance document on mandatory safeguards for broad consent implementation. The mandatory safeguards comprise i) independent review of individual research projects, ii) organizational measures to protect patients from involuntary disclosure of protected information, and iii) comprehensive information for patients and public transparency. This paper further describes the key issues discussed with the relevant authorities, especially the position on additional or alternative consent approaches such as dynamic consent. DISCUSSION Both the resulting broad consent text and the national consensus process are relevant for similar activities internationally. A key challenge of aligning consent documents with the various stakeholders was explaining and justifying the decision to use broad consent and the decision against using alternative models such as dynamic consent. Public transparency for all secondary use projects and their results emerged as a key factor in this justification. While currently largely limited to academic medicine in Germany, the first steps for extending this broad consent approach to wider areas of application, including smaller institutions and medical practices, are currently under consideration.

A.-R. Lagos, I. Kordonis, G.P. Papavassilopoulos

The choices of a population to apply social distancing are modeled as a Nash game, where the agents determine their social interactions. The interconnections among the agents are modeled by a network. The main contribution of this work is the study of an agent-based epidemic model coupled with a social distancing game, which are both determined by the networked structure of human interconnections. The information available to the agents plays a crucial role. We examine the case that the agents know exactly the health states of their neighbors and the case they have only statistical information for the global prevalence of the epidemic. The agents are considered to be myopic, and thus, the Nash equilibria of static games for each day are studied. Through theoretical analysis, we characterize these Nash equilibria and we propose algorithms to compute them. Interestingly, in the case of statistical information the equilibrium strategies for an agent, at each day, are either full isolation or no social distancing at all. Through experimental studies, we observe that in the case of local information, the agents can significantly affect the prevalence of the epidemic with low social distancing, while in the other case, they can also affect the prevalence of the epidemic, but they have to pay the burden of not being well informed by applying strict social distancing. Moreover, the effects of the network structure, the virus transmissibility, the number of vulnerable agents, the health care system capacity and the information quality (fake news) are discussed and relevant simulations are provided.

Anya Topiwala, Klaus P. Ebmeier, Thomas Maullin-Sapey et al.

Moderate alcohol consumption is widespread but its impact on brain structure and function is contentious. The relationship between alcohol intake and structural and functional neuroimaging indices, the threshold intake for associations, and whether population subgroups are at higher risk of alcohol-related brain harm remain unclear. 25,378 UK Biobank participants (mean age 54.9 ± 7.4 years, 12,254 female) underwent multi-modal MRI 9.6 ± 1.1 years after study baseline. Alcohol use was self-reported at baseline (2006–10). T1-weighted, diffusion weighted and resting state images were examined. Lower total grey matter volumes were observed in those drinking as little as 7–14 units (56–112 g) weekly. Higher alcohol consumption was associated with multiple markers of white matter microstructure, including lower fractional anisotropy, higher mean and radial diffusivity in a spatially distributed pattern across the brain. Associations between functional connectivity and alcohol intake were observed in the default mode, central executive, attention, salience and visual resting state networks. Relationships between total grey matter and alcohol were stronger than other modifiable factors, including blood pressure and smoking, and robust to unobserved confounding. Frequent binging, higher blood pressure and BMI steepened the negative association between alcohol and total grey matter volume. In this large observational cohort study, alcohol consumption was associated with multiple structural and functional MRI markers in mid- to late-life.

Theresa M. Coles, Adrian F. Hernandez, Bryce B. Reeve et al.

Abstract Objectives There has been limited success in achieving integration of patient-reported outcomes (PROs) in clinical trials. We describe how stakeholders envision a solution to this challenge. Methods Stakeholders from academia, industry, non-profits, insurers, clinicians, and the Food and Drug Administration convened at a Think Tank meeting funded by the Duke Clinical Research Institute to discuss the challenges of incorporating PROs into clinical trials and how to address those challenges. Using examples from cardiovascular trials, this article describes a potential path forward with a focus on applications in the United States. Results Think Tank members identified one key challenge: a common understanding of the level of evidence that is necessary to support patient-reported outcome measures (PROMs) in trials. Think Tank participants discussed the possibility of creating general evidentiary standards depending upon contextual factors, but such guidelines could not be feasibly developed because many contextual factors are at play. The attendees posited that a more informative approach to PROM evidentiary standards would be to develop validity arguments akin to courtroom briefs, which would emphasize a compelling rationale (interpretation/use argument) to support a PROM within a specific context. Participants envisioned a future in which validity arguments would be publicly available via a repository, which would be indexed by contextual factors, clinical populations, and types of claims. Conclusions A publicly available repository would help stakeholders better understand what a community believes constitutes compelling support for a specific PROM in a trial. Our proposed strategy is expected to facilitate the incorporation of PROMs into cardiovascular clinical trials and trials in general.

Masatoshi Ukezono, Yuji Takano

The data presented in this article is from a paper entitled “An experimental task to examine the mirror neuron system in mice: Laboratory mice understand the movement intentions of other mice based on their own experience” (Ukezono and Takano, 2021). This article contains individual data on reaching behavior for reward in social situations in mice. In the reaching room, the mice first learned how to acquire food by reaching their limbs. The mice that had learned reaching were placed in an observation room where they could observe the reaching activity of another mouse in the reaching room. The data includes all animals’ properties and conditions, the pairing state of another mouse (cage mate or non-cage mate), and a set of behavioral analyses. Our data have the potential to be reused for analyzing interaction behaviors of mice placed in front of rewards and developing experiments for behavioral neuroscience research on the mirror neuron system in mice.

D. V. Andreev, A. V. Gorkovoy, A. V. Dzagashtokova et al.

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Olayinka Oluwatosin Abegunde, Esther Titilayo Akinlabi, Philip Oluseyi Oladijo

The datasets in this article are supplementary to the corresponding research article [1, 2]. The planar morphology and topography of TiC thin films coated on commercially pure Titanium (CpTi) grown by RF magnetron sputtering were investigated using Field emission scanning electron microscope (FESEM) and Atomic force microscope (AFM). The mechanical properties such as Hardness and Young Modulus of the thin film coating was studied using Nanohardness. Furthermore, grazing incidence X-ray diffractometer (GIXRD) and Raman spectroscopy were used to analyse the structural and composition of the TiC thin film coating. Keywords: RF magnetron sputtering, TiC thin film, Field emission scanning electron microscope (FESEM), Atomic force microscope (AFM), Grazing incidence X-ray diffractometer (GIXRD), Raman spectroscopy, Nanohardness

Ryohei Takahashi, Yuya Kajikawa