Large Language Model (LLM)-based agents that plan, use tools and act has begun to shape healthcare and medicine. Reported studies demonstrate competence on various tasks ranging from EHR analysis and differential diagnosis to treatment planning and research workflows. Yet the literature largely consists of overviews which are either broad surveys or narrow dives into a single capability (e.g., memory, planning, reasoning), leaving healthcare work without a common frame. We address this by reviewing 49 studies using a seven-dimensional taxonomy: Cognitive Capabilities, Knowledge Management, Interaction Patterns, Adaptation & Learning, Safety & Ethics, Framework Typology and Core Tasks & Subtasks with 29 operational sub-dimensions. Using explicit inclusion and exclusion criteria and a labeling rubric (Fully Implemented, Partially Implemented, Not Implemented), we map each study to the taxonomy and report quantitative summaries of capability prevalence and co-occurrence patterns. Our empirical analysis surfaces clear asymmetries. For instance, the External Knowledge Integration sub-dimension under Knowledge Management is commonly realized (~76% Fully Implemented) whereas Event-Triggered Activation sub-dimenison under Interaction Patterns is largely absent (~92% Not Implemented) and Drift Detection & Mitigation sub-dimension under Adaptation & Learning is rare (~98% Not Implemented). Architecturally, Multi-Agent Design sub-dimension under Framework Typology is the dominant pattern (~82% Fully Implemented) while orchestration layers remain mostly partial. Across Core Tasks & Subtasks, information centric capabilities lead e.g., Medical Question Answering & Decision Support and Benchmarking & Simulation, while action and discovery oriented areas such as Treatment Planning & Prescription still show substantial gaps (~59% Not Implemented).
Small language models (SLMs) in the 100M-10B parameter range increasingly power production systems, yet whether they possess the internal emotion representations recently discovered in frontier models remains unknown. We present the first comparative analysis of emotion vector extraction methods for SLMs, evaluating 9 models across 5 architectural families (GPT-2, Gemma, Qwen, Llama, Mistral) using 20 emotions and two extraction methods (generation-based and comprehension-based). Generation-based extraction produces statistically superior emotion separation (Mann-Whitney p = 0.007; Cohen's d = -107.5), with the advantage modulated by instruction tuning and architecture. Emotion representations localize at middle transformer layers (~50% depth), following a U-shaped curve that is architecture-invariant from 124M to 3B parameters. We validate these findings against representational anisotropy baselines across 4 models and confirm causal behavioral effects through steering experiments, independently verified by an external emotion classifier (92% success rate, 37/40 scenarios). Steering reveals three regimes -- surgical (coherent text transformation), repetitive collapse, and explosive (text degradation) -- quantified by perplexity ratios and separated by model architecture rather than scale. We document cross-lingual emotion entanglement in Qwen, where steering activates semantically aligned Chinese tokens that RLHF does not suppress, raising safety concerns for multilingual deployment. This work provides methodological guidelines for emotion research on open-weight models and contributes to the Model Medicine series by bridging external behavioral profiling with internal representational analysis.
Ibrahim Luberenga, Rosco Kasujja, Lenny T Vasanthan
et al.
Objectives Workplace-based mental health awareness programmes are increasingly promoted to support employee well-being; however, evidence from low-income and middle-income countries (LMICs) remains fragmented. This scoping review aimed to map and synthesise the characteristics of workplace-based mental health awareness programmes implemented in LMICs.Design Scoping review.Data sources Peer-reviewed studies published between 2000 and 2024 were identified through systematic searches of major electronic databases.Eligibility criteria Studies were eligible if they described or evaluated mental health awareness programmes delivered in workplace settings among adult workers in LMICs.Data extraction and synthesis Data were charted and synthesised descriptively, focusing on programme characteristics, delivery modalities, study designs and outcome domains.Results 66 studies were included, with most published between 2020 and 2024 (n=52). Programmes were implemented across Asia (n=26), the Middle East (n=27), Africa (n=12) and North America (Dominican Republic; n=1). Interventions were delivered both online and in person and employed quantitative, qualitative and mixed-methods designs. Outcome domains assessed included emotional, psychological, physical, social and work-related well-being, with commonly measured outcomes such as anxiety, stress, depression and burnout.Conclusions Mental health awareness programmes in LMIC workplace settings are implemented unevenly and evaluated using heterogeneous outcome measures. More rigorous evaluation designs and culturally tailored approaches are needed to strengthen the evidence base and support effective workplace mental health interventions in LMIC contexts.
ABSTRACT Painful muscle spasms are characterized by sudden onset of involuntary muscle contractions accompanying severe pain. The occurrence of painful muscle spasms after total hip arthroplasty (THA) is extremely rare in clinical practice. We report a case in which a patient developed painful muscle spasms after THA, and symptoms were controlled by oxcarbazepine.
Konstantinos Sechidis, Cong Zhang, Sophie Sun
et al.
Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing important decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individual treatment effect as a basis. To estimate this effect, we use a Double Robust (DR) learner to infer a pseudo-outcome that reflects the causal contrast. This pseudo-outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare our DR-learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis. By integrating these methods with the recently proposed WATCH workflow (Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors), we provide a robust framework for analyzing TEH, offering insights that enable more informed decision-making in this challenging area.
Ali A. Melliti, Raymond Van de Berg, Evangelos Anagnostou
et al.
BackgroundPatients with vestibular and ocular motor disorders often perceive oscillopsia, diplopia or visual hallucinations as their chief complaint. However, they often struggle with verbalizing these subjective ocular motor and visual-perceptual signs precisely, which complicates a correct diagnostic classification of the suspected pathogenic mechanism.MethodsIn this multinational and cross-cultural feasibility study, a novel pictogram-based scale of 10 common ocular motor and visual-perceptual symptoms (called Pictogram Ocular Motor and Visual-Perceptual Symptom Scale, POVSS) was developed and validated. Healthcare professionals with or without expertise in neuro-ophthalmology and neuro-otology, representing a broad range of nationality and primary languages, were asked to match pictograms with medical symptoms (specialists) or a simple English symptom description (non-specialists).ResultsA total of 174 participants (112 specialists, 62 non-specialists) from 30 nationalities evaluated the POVSS. On average, specialists reached a score of 9.7 out of 10 (SD = 0.5; 95% CI: 9.6–9.8) in matching symptoms and pictograms. Non-specialists achieved a mean score of 7.9 (SD = 2.3; 95% CI: 7.3–8.5) in accurately matching pictograms to simple English descriptions. In the specialist group, all pictograms met the common ISO quality standards, whereas in the non-specialist group, 8 out of 10 met the standards. While a significant difference in performance was found between the two groups, success rates did not differ between male and female participants.ConclusionVisual-perceptual symptoms originating from common vestibular and ocular motor disorders could be reliably identified using the POVSS by healthcare professionals, independent of participant nationality, or gender. Further research is needed to test the clinical applicability of the POVSS in different patient care settings.
Garibaldi Riccardo, Lustig Sébastien, Vella-Baldacchino Martinique
et al.
Introduction: Osteoporosis is a prevalent and often underdiagnosed condition that significantly increases the risk of fragility fractures. Dual-energy X-ray absorptiometry (DXA) is the standard diagnostic tool; however, many patients remain unscreened. Preoperative computed tomography (CT) scans obtained for robotic-assisted total knee arthroplasty (TKA) planning present an opportunity for opportunistic osteoporosis screening without additional radiation exposure. Methods: A retrospective observational study was conducted on 637 patients (307 males, 330 females) who underwent robotic-assisted TKA between January 2023 and December 2024. Preoperative CT scans were analyzed using quantitative computed tomography (QCT) software to determine T-scores, Z-scores, and percentage of bone mineral density (BMD) relative to a young-adult reference. Patients were categorized as normal (T-score ≥ −1.0), osteopenic (−2.5 < T-score < −1.0), or osteoporotic (T-score ≤ −2.5). Results: Among 597 patients with available T-score data, 41.0% were classified as normal, 32.3% as osteopenic, and 26.6% as osteoporotic. Notably, 37.0% of female patients were osteoporotic compared to 15.3% of male patients. Bone density parameters declined progressively with age, with females over 80 years exhibiting a mean T-score of −2.53 and BMD at 68.25% of the young-adult reference. Discussion: Opportunistic screening using preoperative CT scans in robotic-assisted TKA patients reveals a high prevalence of undiagnosed low BMD, particularly among elderly women. Integrating QCT analysis into the preoperative workflow may facilitate early identification of at-risk individuals, informing surgical planning and enabling timely interventions to improve bone health.
Moritz Blumenthal, Chiara Fantinato, Christina Unterberg-Buchwald
et al.
Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via non-linear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the FoV and to focus the k-space based SSDU loss on the region of interest. NLINV-Net based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. Results: NLINV-Net based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir-based focussed loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real-time imaging. For quantitative imaging, T1-maps reconstructed using NLINV-Net show similar quality as PI-CS reconstructions, but NLINV-Net does not require slice-specific tuning of the regularization parameter. Conclusion: NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.
Md Abrar Jahin, Md. Akmol Masud, M. F. Mridha
et al.
Heart failure is a leading cause of global mortality, necessitating improved diagnostic strategies. Classical machine learning models struggle with challenges such as high-dimensional data, class imbalances, poor feature representations, and a lack of interpretability. While quantum machine learning holds promise, current hybrid models have not fully exploited quantum advantages. In this paper, we propose the Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network (KACQ-DCNN), a novel hybrid architecture that replaces traditional multilayer perceptrons with Kolmogorov-Arnold Networks (KANs), enabling learnable univariate activation functions. Our KACQ-DCNN 4-qubit, 1-layer model outperforms 37 benchmark models, including 16 classical and 12 quantum neural networks, achieving an accuracy of 92.03%, with macro-average precision, recall, and F1 scores of 92.00%. It also achieved a ROC-AUC of 94.77%, surpassing other models by significant margins, as validated by paired t-tests with a significance threshold of 0.0056 (after Bonferroni correction). Ablation studies highlight the synergistic effect of classical-quantum integration, improving performance by about 2% over MLP variants. Additionally, LIME and SHAP explainability techniques enhance feature interpretability, while conformal prediction provides robust uncertainty quantification. Our results demonstrate that KACQ-DCNN improves cardiovascular diagnostics by combining high accuracy with interpretability and uncertainty quantification.
Amir M. Mansourian, Vladimir Somers, Christophe De Vleeschouwer
et al.
Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method outperforms all existing tracking methods on the challenging SoccerNet tracking dataset.
Théo Sourget, Michelle Hestbek-Møller, Amelia Jiménez-Sánchez
et al.
The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models can use non-relevant parts of images, also called spurious correlations or shortcuts, to obtain high performance on benchmark datasets but fail in real-world scenarios. In this work, we challenge the capacity of convolutional neural networks (CNN) to classify chest X-rays and eye fundus images while masking out clinically relevant parts of the image. We show that all models trained on the PadChest dataset, irrespective of the masking strategy, are able to obtain an Area Under the Curve (AUC) above random. Moreover, the models trained on full images obtain good performance on images without the region of interest (ROI), even superior to the one obtained on images only containing the ROI. We also reveal a possible spurious correlation in the Chaksu dataset while the performances are more aligned with the expectation of an unbiased model. We go beyond the performance analysis with the usage of the explainability method SHAP and the analysis of embeddings. We asked a radiology resident to interpret chest X-rays under different masking to complement our findings with clinical knowledge. Our code is available at https://github.com/TheoSourget/MMC_Masking and https://github.com/TheoSourget/MMC_Masking_EyeFundus
Younes Boulaguiem, Luca Insolia, Maria-Pia Victoria-Feser
et al.
Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal $100(1-2α$)\% confidence intervals for the difference in means between the two conditions of interest lie within pre-defined lower and upper equivalence limits. This procedure, known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate $α$-TOST, that consists in a correction of $α$, the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define $α^{\star}$, the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between $α^{\star}$ and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate $α$-TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses -- i.e.,~relatively small sample sizes, unknown and heterogeneous variances, and different correlation structures -- and show the superior finite-sample properties of the multivariate $α$-TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters.
Amirali Jafarnezhadgero, Ehsan Fakhri Mirzanag, Hamed Sheikhalizadeh
et al.
Being overweight can influence the occurrence of pronated feet (PF). This research aimed to assess the interaction effect of overweight and PF along with sex on the frequency content of ground reaction forces (GRFs). 104 young male and female adults were allocated to four groups: normal body-mass-index/normal feet, normal body-mass-index/PF, excessive weight/normal feet, and excessive weight/PF. Subjects ran at constant speed over the walkway while an embedded force plate was located at the midpoint of the walkway. GRFs were recorded during 20 running trials. Findings demonstrated the significant main effect of “sex” (P
Aleksandar Kovačević, Bojana Bašaragin, Nikola Milošević
et al.
Background: Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.e. the process of removing PHI is a critical step in making EHR data accessible. Natural language processing has repeatedly demonstrated its feasibility in automating the de-identification process. Objectives: Our study aims to provide systematic evidence on how the de-identification of clinical free text has evolved in the last thirteen years, and to report on the performances and limitations of the current state-of-the-art systems. In addition, we aim to identify challenges and potential research opportunities in this field. Methods: A systematic search in PubMed, Web of Science and the DBLP was conducted for studies published between January 2010 and February 2023. Titles and abstracts were examined to identify the relevant studies. Selected studies were then analysed in-depth, and information was collected on de-identification methodologies, data sources, and measured performance. Results: A total of 2125 publications were identified for the title and abstract screening. 69 studies were found to be relevant. Machine learning (37 studies) and hybrid (26 studies) approaches are predominant, while six studies relied only on rules. Majority of the approaches were trained and evaluated on public corpora. The 2014 i2b2/UTHealth corpus is the most frequently used (36 studies), followed by the 2006 i2b2 (18 studies) and 2016 CEGS N-GRID (10 studies) corpora.
Santiago Hernández-Orozco, Abicumaran Uthamacumaran, Francisco Hernández-Quiroz
et al.
For more than two decades, advances in personalised medicine and precision healthcare have largely been based on genomics and other omics data. These strategies aim to tailor interventions to individual patient profiles, promising greater treatment efficacy and more efficient allocation of healthcare resources. Here, we show that widely collected common haematologic markers can reliably predict and discriminate individual chronological age and health status from even noisy sources. Our analysis includes synthetic and real retrospective patient data, including medically relevant and extreme cases, and draws on more than 100\,000 complete blood count records over 13 years from the United States Centers for Disease Control and Prevention's National Health and Nutrition Examination Survey (CDC NHANES). We combine fully explainable risk assessment scores with machine and deep learning techniques to focus on clinically significant patterns and characteristics without functioning purely as a ''black-box model allowing interpretation and control. We validated the results with the UK Biobank, a larger cohort independent of the CDC NHANES and with very different collection techniques, the former a survey and the second a longitudinal study. Unlike current biological ageing indicators, this approach may offer rapid, and scalable implementations of personalised, precision and predictive approaches to healthcare and medicine without or before requiring other specialised, uncommon or costly tests.
Marika Polatowska, Zuzanna Czudy, Hanna Dominik
et al.
Introduction: Obesity and overweight are common diseases affecting both children and adults. They cause a number of health consequences, both physical and mental. The diseases underlying obesity include hypertension, type II diabetes and selected cancers. The etiology is multifactorial, resulting from environmental, behavioral and genetic factors.
Aim of the study: The aim of our study is to summarize the therapeutic options available to overweight and obese people. We paid special attention to the available pharmacology and surgical treatment. We compared these methods in terms of effectiveness, possible complications and side effects.
Materials and methods: The literature available in the PubMed and Google Scholar databases was reviewed, using the following keywords: "obesity treatment", "obesity pharmacotherapy", "bariatric surgery".
Conclusions: There are many treatment options for people who have not improved with diet and increased physical activity alone. The availability of both pharmacotherapy and bariatric surgery enables effective treatment of obesity and overweight tailored to the needs of patients.
Introduction
Nonprofit sports organisations are valuable sports providers in most European countries. In 2017, there were over 60 million European active sports club members (Nagel et al., 2020). Through the consumption behaviour of this great number of people practicing sports, sport might have a negative impact on the environment (McCullough et al., 2020). Therefore, national sports federations (NSFs), overarching the sports clubs and engaging in sports policy issues, could be crucial when it comes to taking measures concerning the environmental sustainability of sports. Indeed, several NSFs have already launched programmes for environmental sustainability (e.g. Swiss Hang- and Paragliding Association [SHV]). This might be somehow surprising, since the nonprofit organised sport is traditionally primarily committed to the interests of its members and the sport as its core business (Thiel & Mayer, 2009). Thus, the following study addresses the questions, to what extend policies of environmental sustainability appear on the agendas of NSFs and which driving factors are relevant for its agenda setting, formulation and subsequent decision making. Knowledge about those processes is especially important since they are prerequisites for the implementation of environmental policies.
Literature review and theoretical background
Concerning nonprofit sports organisations and sustainable development, especially the field of social sustainability (e.g. Nagel et al., 2020) has been widely researched. Environmental sustainability however has only limitedly been analysed in this context. Describing the current state of commitment of NSFs to environmental sustainability, it has been found that Belgian NSFs show a rather low commitment, whereas low-intensity initiatives (e.g. recycling of sport equipment) constitute the majority of the identified actions (Hugaerts et al., 2022). The same pattern could be shown in Scandinavia. Sandvik and Seippel (2022) explain this partly with the absence of NSFs’ perceived urgence of environmental problems with direct consequences for the associations’ activities and the lack of institutional pressures. However, so far, there is hardly any knowledge about which factors are relevant for environmental policies being set on NSFs’ agendas, formulated, and decided upon, even though those processes are important as they precede the actual implementation of policies.
Considering agenda setting, policy formulation and decision making processes, this study is based on the Multiple Streams Approach with two coupling phases (MSA) of Herweg et al. (2015). For “an ideas’ time to come”, the approach identifies the coupling of three different processes as important. Policies arise, when issues are perceived as problematic (problem stream; e.g. image issues) and depend on the political context of the policy and agenda (political stream, e.g. composition of board). The policy stream contains existing ideas and enables the survival of certain ideas (e.g. idea of developing a climate strategy). Finally, policy entrepreneurs’ agency is necessary to couple the streams and create agenda windows, which allow items to rise onto the decision agenda. In a following second phase, the re-coupling of the same streams and engagement of policy entrepreneurs enables policies to be formulated, decided upon, and eventually be implemented.
Methods
To observe those processes, we decided to conduct an in-depth qualitative case study with a NSF, that has recently discussed, formulated, and decided upon environmental policies. Since the hang- and paragliding sport is depending on a natural environment affected by climate change, the SHV seemed to be an interesting case for our study, where such processes might already have taken place. The association is committed to the interests and sustainable practice of free flight. It has 112 club and 20,000 individual members, maintains an office with 14 employees and is headed by a board of directors, currently with seven members.
Document- and archive entries allowed us a first overview of existing measures of environmental sustainability and the appearance of the topic on the NSF’s agenda. We conducted semi-structured expert interviews with six decision makers of the SHV and with a representative of the umbrella organisation of the Swiss sports system to gain a deeper understanding of the agenda setting, formulation and decision making of environmental policies in the SHV. We then applied causation coding (Miles et al., 2020) to analyse the data, considering a data-led as well as a theory-based coding process along the concept of the MSA.
Findings
The SHV's commitment to environmental sustainability is based primarily on two pillars: the protection of biodiversity and access to nature on one hand and climate commitment on the other. Whereas voluntary agreements about wildlife rest areas have been made since 1995, climate protection policies are broadly discussed as a part of the strategy and implemented on an operative basis in different departments since the creation of the position of an environmental officer in 2017.
If not forced by external political pressure (e.g. flight restrictions), the agenda setting of environmental sustainability (e.g. climate commitment) in the SHV seems to be pushed primarily by the engagement of individual policy entrepreneurs. Policy entrepreneurs thereby mainly highlight the importance of addressing the issue to ensure the long-term survival of the sport and the association (problem stream). The perceived member interests seem to be able to facilitate or impede the agenda setting of the topic (political stream). Nevertheless, members do not appear to be the central entrepreneurs when it comes to actively promoting policies of environmental sustainability onto the agenda. The ideas for such policies rather seem to be found in the practice of similar other NSFs (policy stream).
The board of management seems to be less crucial for this process of agenda setting. When it comes to formulation and decision making however, the importance of the board of management is structurally implied. Even though the General Assembly approves amendments and the management of the board, the latter issues directives. Since members of the board do not yet seem to have the necessary knowledge about environmental sustainability to feel competent enough to make the required decisions, thorough information provided by the environmental officers appears to be important. Actors of the association describe policy formulation as a long process, where, in order to make the members of the board feel comfortable enough to make decisions, policies of environmental sustainability “must become a topic first over time”. When it comes to decision making about environmental policies, the (political) background of the members of the board are crucial and can promote or hinder certain decisions. Nevertheless, after getting enough information, suggestions from the administrative office are mostly accepted.
Discussion
Therefore, engaged policy entrepreneurs seem to be crucial for agenda setting, formulation and, through the impact of their suggestions, decision making processes of environmental policies in the SHV. Policy entrepreneurs do show engagement for their favoured policies even if they are not responsible for that specific topic in the association, but the structural implication of positions in connection with sport- and socio-political developments can additionally promote environmental policies. Even though the agenda setting of such policies does not seem to be a bottom-up process, consistent with the understanding of NSFs as interest-oriented organisations, perceived member interests are central in all three processes. Thereby functional objectives (e.g. enabling sport) are predominant to normative reasons (e.g. environmental responsibility). Furthermore, there seem to be processes that might be explained by the concept of mimetic isomorphism (DiMaggio & Powell, 1983), i.e. the orientation towards practices of similar NSFs in order to gain legitimacy.
This pilot study allows an in-depth investigation and enables a first review of the used theoretical and methodological approach. It identifies crucial factors when promoting the agenda setting, formulation and decision making for environmental sustainability in NSFs and utilising the potential of the nonprofit organised sports setting and its leverage in society. It is presented as part of a broader follow-up multiple case study with eight NSFs and a cross-case comparison. Further research must be conducted to allow statements about other types of NSFs (e.g. other types of professionalisation, indoor sports) and further stages of the policy cycle, i.e. the implementation of the policies.
References
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
Herweg, N., Huss, C., & Zohlnhöfer, R. (2015). Straightening the three streams: Theorising extensions of the multiple streams framework. European Journal of Political Research, 54(3), 435–449. https://doi.org/10.1111/1475-6765.12089
Hugaerts, I., Scheerder, J., Zeimers, G., Corthouts, J., van de Sype, C., & Könecke, T. (2022). Are sport organisations environmentally sustainable? A website analysis of sport federations in Belgium. European Sport Management Quarterly. Advance online publication. https://doi.org/10.1080/16184742.2022.2093391
McCullough, B. P., Orr, M., & Watanabe, N. M. (2020). Measuring externalities: The imperative next step to sustainability assessment in sport. Journal of Sport Management, 34(5), 393–402. https://doi.org/10.1123/jsm.2019-0254
Miles, M. B., Huberman, A. M., & Saldaña, J. (2020). Qualitative data analysis: A methods sourcebook (4th ed.). SAGE.
Nagel, S., Elmose-Østerlund, K., Ibsen, B., & Scheerder, J. (Eds.). (2020). Functions of Sports Clubs in European Societies: A Cross-National Comparative Study. Springer.
Sandvik, M. R., & Seippel, Ø. (2022). Framing of environmental issues in voluntary sport organizations. Environmental Politics. Advance online publication. https://doi.org/10.1080/09644016.2022.2075152
Thiel, A., & Mayer, J. (2009). Characteristics of voluntary sports clubs management: A sociological perspective. European Sport Management Quarterly, 9(1), 81-98. https://doi.org/10.1080/16184740802461744
Mahmood Alzubaidi, Marco Agus, Khalid Alyafei
et al.
Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care during pregnancy. This systematic survey aims to explore how artificial intelligence (AI) can assist with fetal growth monitoring via Ultrasound (US) image. We used eight medical and computer science bibliographic databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACM Library, Google Scholar, and the Web of Science. We retrieved studies published between 2010 to 2021. Data extracted from studies were synthesized using a narrative approach. Out of 1269 retrieved studies, we included 107 distinct studies from queries that were relevant to the topic in the survey. We found that 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasound images (n=19). Classification is the most used method (n=42), followed by segmentation (n=31), classification integrated with segmentation (n=16) and other miscellaneous such as object-detection, regression and reinforcement learning (n=18). The most common areas within the pregnancy domain were the fetus head (n=43), then fetus body (n=31), fetus heart (n=13), fetus abdomen (n=10), and lastly the fetus face (n=10). In the most recent studies, deep learning techniques were primarily used (n=81), followed by machine learning (n=16), artificial neural network (n=7), and reinforcement learning (n=2). AI techniques played a crucial role in predicting fetal diseases and identifying fetus anatomy structures during pregnancy. More research is required to validate this technology from a physician's perspective, such as pilot studies and randomized controlled trials on AI and its applications in a hospital setting.