Hasil untuk "Medical legislation"

Guillem Pratx, Wensha Yang, Matthew L Scarpelli

This report summarizes changes in federal research funding to the medical physics community between FY24 and FY25. By linking the AAPM membership database with NIH RePORTER records, we quantified the distribution of NIH funding for projects led by AAPM researchers. Although total NIH funding to AAPM members remained relatively stable across the two years, the composition of that funding shifted substantially. Competing (new and renewal) awards declined 50%, driven largely by an 80% collapse in new R01 grants from the National Cancer Institute (NCI). In contrast, noncompeting continuation awards increased by 10%, following a shift in how NIH funds multi-year projects. These changes occurred in the context of widespread disruptions to NIH review and grantmaking, including delayed study sections and more stringent administrative requirements. Federal funding is essential to sustaining innovation, supporting early-stage investigators, and ensuring that patients receive the best possible care. The trends identified here raise concerns about the long-term vitality and stability of the medical physics research pipeline.

Anna Frixou, Efstathios Stiliaris, Costas N. Papanicolas

We present an experimental setup and methodology designed to facilitate high-precision thermal measurements required for infrared medical tomography. The approach which is best suited for the study of specialized hardware phantoms comprises a controlled environmental enclosure, infrared detection, internal thermal reference elements, and a comprehensive data acquisition counting chain and protocol. Temporal and spatial corrections applied to sequential thermal images and panoramic projections reduce measurement fluctuations resulting in measurement uncertainty to approximately 25~mK. The capability to resolve weak surface temperature variations, well below 0.1~K, meets the requirement of medical imaging sensitivity. The methodology was validated using wax phantoms with elevated-temperature sources ($ΔT$ = 1.5 to 10~K). Reconstructed 3D thermal tomographic images of hot spots embedded in hardware phantoms are found to be in quantitative agreement with thermocouple measurements and $μCT$ derived source positions. The results demonstrate that the proposed setup and methodology enable high-precision thermal measurements and establish the feasibility of detecting surface temperature variations below 0.1 K, consistent with low-temperature localized internal contrasts ($ΔT =$ 1-3 K) at subsurface depths of a few centimeters, relevant to biological tissue.

Wiktor Sokół, Bartosz Mika

The article focuses on the initial findings of ongoing research dedicated to the practices of masseurs and physiotherapists. One unique aspect of working in a specific occupation is the social acknowledgement of certain tasks as constituting a distinct job. Establishing the social boundaries of an occupation is an intricate and enduring process. This study focuses on defining the professional standing of physiotherapists by means of official procedures, embedding the field within legal frameworks (legislation from 2015), and forming community structures that safeguard professional limits akin to guilds (the Polish Chamber of Physiotherapists). The mentioned processes were interlinked and generated the formal framework for physiotherapy as an independent medical occupation. In the following study, the authors reconstructed the process of professionalization based on in-depth interviews with experts involved in passing the 2015 bill as well as with active members of the aforementioned Chamber. The study demonstrates how professionalization – defined as the establishment of distinct professional boundaries within subjective worker narratives of similar yet separate job roles – is acknowledged in legal frameworks.

Weihua Mu, Lina Cao

This study extends the Lucas-Washburn theory through non-equilibrium thermodynamic analysis to examine fluid absorption in medical foams used for hemorrhage control. As a universal model for capillary flow in porous media, the theory demonstrated strong agreement with experimental results, confirming its semi-quantitative accuracy. Minor deviations, likely due to material heterogeneity, were observed and explained, enhancing the theory's applicability to real-world conditions. Our findings underscore the universality of the Lucas-Washburn framework and provide valuable insights for optimizing the design of medical foams, ultimately contributing to more effective bleeding control solutions in clinical applications.

Tim J. M. Jaspers, Ronald L. P. D. de Jong, Yasmina Al Khalil et al.

Over the past decade, computer vision applications in minimally invasive surgery have rapidly increased. Despite this growth, the impact of surgical computer vision remains limited compared to other medical fields like pathology and radiology, primarily due to the scarcity of representative annotated data. Whereas transfer learning from large annotated datasets such as ImageNet has been conventionally the norm to achieve high-performing models, recent advancements in self-supervised learning (SSL) have demonstrated superior performance. In medical image analysis, in-domain SSL pretraining has already been shown to outperform ImageNet-based initialization. Although unlabeled data in the field of surgical computer vision is abundant, the diversity within this data is limited. This study investigates the role of dataset diversity in SSL for surgical computer vision, comparing procedure-specific datasets against a more heterogeneous general surgical dataset across three different downstream surgical applications. The obtained results show that using solely procedure-specific data can lead to substantial improvements of 13.8%, 9.5%, and 36.8% compared to ImageNet pretraining. However, extending this data with more heterogeneous surgical data further increases performance by an additional 5.0%, 5.2%, and 2.5%, suggesting that increasing diversity within SSL data is beneficial for model performance. The code and pretrained model weights are made publicly available at https://github.com/TimJaspers0801/SurgeNet.

Deepak Raina, SH Chandrashekhara, Richard Voyles et al.

Ultrasound is a commonly used medical imaging modality that requires expert sonographers to manually maneuver the ultrasound probe based on the acquired image. Autonomous Robotic Ultrasound (A-RUS) is an appealing alternative to this manual procedure in order to reduce sonographers' workload. The key challenge to A-RUS is optimizing the ultrasound image quality for the region of interest across different patients. This requires knowledge of anatomy, recognition of error sources and precise probe position, orientation and pressure. Sample efficiency is important while optimizing these parameters associated with the robotized probe controller. Bayesian Optimization (BO), a sample-efficient optimization framework, has recently been applied to optimize the 2D motion of the probe. Nevertheless, further improvements are needed to improve the sample efficiency for high-dimensional control of the probe. We aim to overcome this problem by using a neural network to learn a low-dimensional kernel in BO, termed as Deep Kernel (DK). The neural network of DK is trained using probe and image data acquired during the procedure. The two image quality estimators are proposed that use a deep convolution neural network and provide real-time feedback to the BO. We validated our framework using these two feedback functions on three urinary bladder phantoms. We obtained over 50% increase in sample efficiency for 6D control of the robotized probe. Furthermore, our results indicate that this performance enhancement in BO is independent of the specific training dataset, demonstrating inter-patient adaptability.

Negin Ghamsarian, Javier Gamazo Tejero, Pablo Márquez Neila et al.

Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for semi-supervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps. Since the neural network performance should be invariant to image transformations, we look to this fact to identify uncertain pseudo labels. Indeed, we argue that transformation invariant detections can provide more reasonable approximations of ground truth. Accordingly, we propose a semi-supervised learning strategy for domain adaptation termed transformation-invariant self-training (TI-ST). The proposed method assesses pixel-wise pseudo-labels' reliability and filters out unreliable detections during self-training. We perform comprehensive evaluations for domain adaptation using three different modalities of medical images, two different network architectures, and several alternative state-of-the-art domain adaptation methods. Experimental results confirm the superiority of our proposed method in mitigating the lack of target domain annotation and boosting segmentation performance in the target domain.

Hadi Moheb-Alizadeh, Amir Hossein Sadeghi, Amirreza Sahebi fakhrabad et al.

In recent years, recycling and disposal of end-of-life (EOL) electronic products has attracted considerable attention in response to concerns over resource recovery and environmental impacts of electronic waste (e-waste). In many countries, legislation to make manufacturers responsible for taking e-waste at the end of their useful lives either has been adopted or is being considered. In this paper, by capturing different stages in the life-cycle of EOL electronic products (or, e-waste) generated from private or small-entity users, we develop two different formulations of a reverse logistics network, i.e. system-optimum model and user-optimum model, to estimate both economic and environmental effects of take-back legislation. In this system, e-waste is collected through user drop-off at designated collection sites. While we study the whole reverse logistics network associated with recycling and remanufacturing of e-waste in the system-optimum model and obtain an optimum solution from the policy maker's perspective, we split the logistics network into two distinct parts in the user-optimum model in order to derive an optimum solution from the users' standpoint. Implementing the proposed models on an illustrative example shows how they are capable of estimating the economic and environmental impacts of take-back legislation in various stages of e-waste's life-cycle.

Sky Wei Chee Koh, Hwei Ming Tan, Wayne Han Lee et al.

Objectives: COVID-19 booster uptake has remained poor among healthcare workers (HCWs) despite evidence of improved immunity against the SARS-COV-2 δ (delta) and ο (omicron) variants. Although most studies have used a questionnaire to assess hesitancy, we aimed to identify factors affecting booster hesitancy by examining actual vaccine uptake across time. Methods: COVID-19 vaccination database records were extracted for HCWs working at 7 Singaporean public primary-care clinics between January and December 2021. Data included sex, profession, place of practice, vaccination type, and dates. Time to booster was calculated from the date of vaccination minus the date of eligibility. The χ2 test was used to compare the relationship between first dose and booster hesitancy. The Kaplan-Meier method and the log-rank test were used to evaluate differences in cumulative booster uptake. Multivariate Cox regression was used to investigate predictors of timely booster vaccination. The vaccination rate was charted across time and was corroborated with media releases pertaining to legislative changes. Results: Of 891 primary-care HCWs, 877 (98.9%) were fully vaccinated and 73.8% of eligible HCWs had taken the booster. HCWs were less booster hesitant (median, 16 days; range, 5–31.3) compared to the first dose (median, 39 days; range, 13–119.3). First-dose–hesitant HCWs were more likely to be booster hesitant (OR, 3.66; 95% CI, 2.61–5.14). Adjusting for sex, workplace, and time to first dose, ancillary HCWs (HR, 1.53; 95% CI, 1.03–2.28), medical HCWs (HR, 1.8; 95% CI, 1.18–2.74), and nursing HCWs (HR, 1.8; 95% CI, 1.18–2.37) received boosters earlier than administrative staff. No temporal relationship was observed for booster uptake, legislative changes, or COVID-19 case numbers. Conclusions: Vaccine hesitancy among HCWs had improved from first dose to booster, with timely booster vaccination among medical and nursing staff. Tailored education, risk messaging, and strategic legislation might help reduce delayed booster vaccination. This study was approved by the National Healthcare Group (NHG) Domain Specific Review Board (DSRB), Singapore on December 28, 2021 (Reg No. 2021/01120).

Iraj Daizadeh

Introduction: Since inception, the United States (US) Food and Drug Administration (FDA) has kept a robust record of regulated medical devices (MDs). Based on these data, can we gain insight into the innovation dynamics of the industry, including the potential for industrial transformation? Areas Covered: Using Premarket Notifications (PMNs) and Approvals (PMAs) data, it is shown that from 1976 to 2020 the total composite (PMN + PMA) metric follows a single secular period: 20.5 years (applications peak-to-peak: 1992-2012; trough: 2002) and 26.5 years (registrations peak to peak: 1992 to 2019; trough: 2003), with a peak to trough relative percentage difference of 24% and 28%, respectively. Importantly, PMNs and PMAs independently present as an inverse structure. Expert Opinion: The evidence suggests: MD innovation is driven by a singular secular Kutnets-like cyclic phenomenon (independent of economic crises) derived from a fundamental shift from simple (PMNs) to complex (PMAs) MDs. Portentously, while the COVID-19 crisis may not affect the overriding dynamic, the anticipated yet significant (~25%) MD innovation drop may be potentially attenuated with attentive measures by MD stakeholders. Limitations of this approach and further thoughts complete this perspective.

Benedetto Di Ruzza

Technological tests of Total Ionizing Dose effects are required not only for silicon particle sensors developed in high energy physics experiments, but also for electronic devices and semiconductor elements used in commercial, automotive and space applications. Using x-ray irradiators, these technological tests and studies can be performed not only in facilities explicitly built for this mission, but also in medical or biological research facilities when some minima requirements are satisfied. Generally this irradiation can be performed without interfering with the medical and biological activities of the facility. In this article will be described the minimum instrumentation required for this type of studies and will be given a detailed description of the preparation and realization of a SiPM x-ray irradiation campaign for TID characterization realized in the Italian TIFPA-INFN Trento Center Laboratory in May 2021, using instruments originally realized for medical and biological irradiation.

Yuyuan Liu, Yu Tian, Chong Wang et al.

3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given the high cost to obtain such annotation. Semi-supervised learning (SSL) solve this issue by training models with a large unlabelled and a small labelled dataset. The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data. These perturbations usually keep the spatial input context between views fairly consistent, which may cause the model to learn segmentation patterns from the spatial input contexts instead of the segmented objects. In this paper, we introduce the Translation Consistent Co-training (TraCoCo) which is a consistency learning SSL method that perturbs the input data views by varying their spatial input context, allowing the model to learn segmentation patterns from visual objects. Furthermore, we propose the replacement of the commonly used mean squared error (MSE) semi-supervised loss by a new Cross-model confident Binary Cross entropy (CBC) loss, which improves training convergence and keeps the robustness to co-training pseudo-labelling mistakes. We also extend CutMix augmentation to 3D SSL to further improve generalisation. Our TraCoCo shows state-of-the-art results for the Left Atrium (LA) and Brain Tumor Segmentation (BRaTS19) datasets with different backbones. Our code is available at https://github.com/yyliu01/TraCoCo.

Lorenzo Mercolli, Axel Rominger, Kuangyu Shi

The use of machine learning systems in clinical routine is still hampered by the necessity of a medical device certification and/or by difficulty to implement these systems in a clinic's quality management system. In this context, the key questions for a user are how to ensure reliable model predictions and how to appraise the quality of a model's results on a regular basis. In this paper we first review why the common out-of-sample performance metrics are not sufficient for assessing the robustness of model predictions. We discuss some conceptual foundation for a clinical implementation of a machine learning system and argue that both vendors and users should take certain responsibilities, as is already common practice for high-risk medical equipment. Along this line the best practices for dealing with robustness (or absence thereof) of machine learning models are revisited. We propose the methodology from AAPM Task Group 100 report no. 283 as a natural framework for developing a quality management program for a clinical process that encompasses a machine learning system. This is illustrated with an explicit albeit generic example. Our analysis shows how the risk evaluation in this framework can accommodate machine learning systems independently of their robustness evaluation. In particular, we highlight how the degree of interpretability of a machine learning system can be accounted for systematically within the risk evaluation and in the development of a quality management system.

Louise Milling, Jeannett Kjær, Lars Grassmé Binderup et al.

Abstract Aim This systematic review explored how non-medical factors influence the prehospital resuscitation providers’ decisions whether or not to resuscitate adult patients with cardiac arrest. Methods We conducted a mixed-methods systematic review with a narrative synthesis and searched for original quantitative, qualitative, and mixed-methods studies on non-medical factors influencing resuscitation of out-of-hospital cardiac arrest. Mixed-method reviews combine qualitative, quantitative, and mixed-method studies to answer complex multidisciplinary questions. Our inclusion criteria were peer-reviewed empirical-based studies concerning decision-making in prehospital resuscitation of adults > 18 years combined with non-medical factors. We excluded commentaries, case reports, editorials, and systematic reviews. After screening and full-text review, we undertook a sequential exploratory synthesis of the included studies, where qualitative data were synthesised first followed by a synthesis of the quantitative findings. Results We screened 15,693 studies, reviewed 163 full-text studies, and included 27 papers (12 qualitative, two mixed-method, and 13 quantitative papers). We identified five main themes and 13 subthemes related to decision-making in prehospital resuscitation. Especially the patient’s characteristics and the ethical aspects were included in decisions concerning resuscitation. The wishes and emotions of bystanders further influenced the decision-making. The prehospital resuscitation providers’ characteristics, experiences, emotions, values, and team interactions affected decision-making, as did external factors such as the emergency medical service system and the work environment, the legislation, and the cardiac arrest setting. Lastly, prehospital resuscitation providers’ had to navigate conflicts between jurisdiction and guidelines, and conflicting values and interests. Conclusions Our findings underline the complexity in prehospital resuscitation decision-making and highlight the need for further research on non-medical factors in out-of-hospital cardiac arrest.

V. V. Popov, A. V. Erokhina, S. V. Cherkasova

The article deals with the activities of a psychologist-educator and his role in relation to the problem of student deviation in the digitalisation context and the widespread use of the Internet. A comprehensive analysis of adolescent deviations and various aspects of dealing with students’ destructive behavior and with their parents using knowledge from different subject areas – law, sociology, psychology, medicine, pedagogy, has been conducted. The features and characteristics of adolescence are considered, and the factors influencing the formation of deviant behavior, including medical ones, are identified. The role of the educational organisation’s psychologist in identifying and correcting learners behavior destructive forms is outlined. The necessary ratio between the staff of psychologists and medical specialists and the students number in educational organisations is given. As a result of the analysis of the current legislation, the shortcomings of certain legal provisions and the need to change them in destructive behavior prevention part were noted. Recommendations for professionals in the helping professions have been developed and projected positive results of the outlined activities have been given. The authors’ conceptual findings are applicable to creating a safe and supportive environment for learners, reducing juvenile delinquency and amending legislation.

J. Aneja, Sona Arora

Prior to the Covid-19 pandemic, telemedicine was an upcoming modality of diagnosis and treatment, but was limited by inadequate guidelines and legislation. Now, during the pandemic, policy makers worldwide have found it prudent to set forth new telemedicine guidelines to ease and regulate operations. The Government of India, along with the Board of Governors (in supercession of the Medical Council of India), amended the Indian Medical Council Act, 1956, and published the Telemedicine Practice Guidelines in 2020 to lay down a framework for telemedicine practice in our country. This step will provide a medico-legal context for the delivery of online health services. However, the Guidelines in their present form only partly address the ethical aspects of telemedicine in India. Here, we briefly describe these Guidelines and highlight the ethical concerns surrounding the teleconsultation process in our country. We also suggest possible solutions to these ethical issues surrounding the practice of telemedicine.

K. Lerma, P. Blumenthal

Medical and surgical methods can both be recommended for second trimester abortion (after 12-weeks of gestational age). Induced abortion with a mifepristone and misoprostol regimen is the preferred approach; where mifepristone is not available, misoprostol alone for medical abortion is also effective. Dilation and evacuation (D&E) is the procedure of choice for surgical abortions, and adequate cervical preparation contributes significantly to safety. Availability of drugs and instruments, ability to provide pain control, provider skill and comfort, client preference, cultural considerations, and local legislation all influence the method of abortion likely to be performed in a given setting. Both surgical and modern medical methods are safe and effective when provided by a trained, experienced provider.

Benjamin K Buchanan, David M. Siebert, Monica L. Zigman Suchsland et al.

Background: Sickle cell trait (SCT) has been associated with an increased risk of sudden death in athletes during strenuous exercise. In August 2010, the National Collegiate Athletic Association (NCAA) began requiring athletes to be screened for SCT, provide proof of SCT status, or sign a waiver and launched an educational campaign for athletes, coaches, and medical staff. The impact of this program is unknown. The purpose of this study was to determine the incidence of death associated with sickle cell trait (daSCT) in NCAA athletes before and after legislation. Hypothesis: NCAA SCT legislation will decrease the incidence of daSCT. Study Design: Observational study. Level of Evidence: Level 2. Methods: A database of NCAA athlete deaths from 2000 to 2019 was reviewed for daSCT. A total of 8,309,050 athlete-years (AY) were included. Incidence of death was calculated before and after legislation. Results: The incidence of daSCT in Division I (DI) football athletes before legislation (n = 9) was 1:28,145 AY and after legislation (n = 1) was 1:250,468 AY (relative risk [RR], 0.112; 95% CI, 0.003-0.811; P = 0.022), an 89% reduction in risk after legislation was enacted. The incidence of daSCT in African American DI football athletes before legislation (n = 9) was 1:12,519 AY and after legislation (n = 1) was 1:118,464 AY (RR, 0.106; 95% CI, 0.002-0.763; P = 0.017), also an 89% risk reduction after legislation was enacted. For all NCAA athletes, the incidence of daSCT was 1:489,749 AY before legislation (n = 10) and 1:1,705,780 AY after legislation (n = 2) (RR, 0.288; 95% CI, 0.031-1.347; P = 0.146). Conclusion: The incidence of daSCT in DI football athletes has decreased significantly since legislation was enacted. Cases of daSCT outside of football are rare. It is unclear whether the decrease is related to screening for SCT, education, or both. Clinical Relevance: This is the first evidence that NCAA SCT legislation may save lives.

Katherine Side

In the context of Ireland’s new legislation governing abortion, I outline and examine the spatial consequences of political decision-making. I argue that Ireland’s new abortion law and its clinical guidance permit travel for some pregnant people but impose fixity on others. I analyse the spatial consequences of legal limitations, including non-medically necessary delays in care and medical control of medication abortions, that necessitate travel for abortion. I demonstrate how current laws fix some pregnant people in place, including diverse migrant populations within Ireland, with no possibilities for abortion-related travel. This critique of the ‘new’ law demonstrates the Irish state’s continued political and medical control of abortion.

Juan M Garcia-Gomez

Space agencies and private companies prepare the beginning of human space exploration for the 2030s with missions to put the first human on the Mars surface. The absence of gravity and radiation, along with distance, isolation and hostile environments, are expected to increase medical events where previously unseen manifestations may arise. The current healthcare strategy based on telemedicine and the possibility to stabilize and transport the injured crewmember to a terrestrial definitive medical facility is not applicable in exploration class missions. Therefore, the need for deploying the full autonomous capability to solve medical emergencies may guide the design of future onboard healthcare systems. We present ten basic principles and concept design of a software suite to bring onboard decision support to help the crew dealing with medical emergencies taking into consideration physiological disturbances in space and spaceflight restrictions. 1) give real-time support for emergency medical decision making, 2) give patient-specific advice for executive problem-solving, 3) take into account available information from life support and monitoring of crewmembers, 4) be fully autonomous from remote facilities, 5) continuously adapt predictions to physiological disturbance and changing conditions, 6) optimize emergency medical decision making in terms of mission fundamental priorities, 7) take into account medical supplies and equipment on board, 8) apply health standards for the level of care V, 9) implement ethics responsibilities for spaceflights, and 10) apply ethical standards for artificial intelligence. Based on these principles, we propose an autonomous clinical decision support system (CDSS) to provide real-time advice for emergency medical interventions on board of space exploration missions.