A cognitive task analysis of final year nursing students' situation awareness in simulated deteriorating patient events: A mixed methods study.

AIM: The aim of this study was to: (1) use cognitive task analysis to describe final year nursing students situation awareness in recognising, responding and escalating care of deteriorating patients in ward settings; and (2) make recommendations for training and practice. DESIGN: A mixed methods cognitive task analysis with a convergent triangulation design. METHOD: Data collection involved observations of 33 final year nursing students in simulated deteriorating patient scenarios and retrospective cognitive interviews. A process tracing technique was applied to identify the cues to deterioration participants perceived; how cue perception altered as situational demands increased; the extent that participants made connections between perceived cues and reached a situational understanding; and the factors that influenced and constrained participants situation awareness. Qualitative and quantitative findings are woven together and presented using descriptive statistics, illustrative quotations and timeline extractions. RESULTS: The median cue perception was 65.4% and 57.6% in the medical and surgical scenarios, respectively. Perception was negatively influenced by incomplete vital sign monitoring as situations escalated; limited physical assessments; passive scanning behaviours; poor task automaticity; and excessive cognitive demands. Incomplete perception, poor cue integration and underdeveloped mental models influenced situational understanding. Escalation calls did not always accurately reflect situations and a reporting mindset was evident. Clinical exposure to deteriorating patients was described as variable and opportunistic. REPORTING METHOD: The study is reported in accordance with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. PATIENT OR PUBLIC CONTRIBUTION: Patients and public were not involved in this research.

Rome-Celsus (ca 25BC to ca. 50AD).

Celsus gave an adequate description of the bones and sutures of the calvarium. His classification of injuries was simple including fissures and depressions. He is the first to relate specific symptoms to specific tissue injury. In addition, he was aware that fractures could be present in the absence of typical findings. He was also the first to note the meningeal vessels could rupture producing severe localized pain. His treatment was more conservative than that of Hippocrates. Plasters were to be used and if there was no deterioration trepanation was avoided. He described the use of the crown trepan (modiolus) and the instrument with a smaller tip which expands rapidly to prevent penetration. He also described the technique of rotating the trepan between the palms. His description of operations for depressed fractures were unusually clear and relevant even by modern standards. His wound care is strikingly different from that of Hippocrates as he advocates various dressings soaked in vinegar and as time passes plasters should be softened with rose oil. Regrettably, Celsus influence would not be felt until the time of the Renaissance, because his texts were lost. However, his "De Medicina" was rediscovered and became the first medical text to be published using the new moveable type printing press, in the year 1478. Thus, his influence was minimal in the Arabic world and the early Renaissance. Thereafter it was profound.

Non-technical skills needed by medical disaster responders- a scoping review.

BACKGROUND: There is no universal agreement on what competence in disaster medicine is, nor what competences and personal attributes add value for disaster responders. Some studies suggest that disaster responders need not only technical skills but also non-technical skills. Consensus of which non-technical skills are needed and how training for these can be provided is lacking, and little is known about how to apply knowledge of non-technical skills in the recruitment of disaster responders. Therefore, this scoping review aimed to identify the non-technical skills required for the disaster medicine response. METHOD: A scooping review using the Arksey & O´Malley framework was performed. Structured searches in the databases PuBMed, CINAHL Full Plus, Web of Science, PsycInfo and Scopus was conducted. Thereafter, data were structured and analyzed. RESULTS: From an initial search result of 6447 articles, 34 articles were included in the study. These covered both quantitative and qualitative studies and different contexts, including real events and training. The most often studied real event were responses following earthquakes. Four non-technical skills stood out as most frequently mentioned: communication skills; situational awareness; knowledge of human resources and organization and coordination skills; decision-making, critical-thinking and problem-solving skills. The review also showed a significant lack of uniform use of terms like skills or competence in the reviewed articles. CONCLUSION: Non-technical skills are skills that disaster responders need. Which non-technical skills are most needed, how to train and measure non-technical skills, and how to implement non-technical skills in disaster medicine need further studies.

Support for a hypothetical sugar-sweetened beverage tax in Taiwan.

Sugar-sweetened beverages (SSBs) are drinks that contain added sugar or sweeteners and provide calories with no additional nutrients, and some countries have imposed additional taxes on the SSBs to reduce consumption, which is considered an SSB tax policy. This study used a cross-sectional online survey to examine the patterns of public support for an SSB tax in Taiwan. The sample included 1617 adults aged ≥ 20 years, who answered the survey questionnaire between May 2020 and April 2021. The respondents were recruited using convenience sampling, but sampling weights were applied to represent the Taiwanese population. Generalized ordered logit models with sampling weights were used to examine the correlates of public support for an SSB tax. Results showed that ~60% of the respondents supported the SSB tax and 47% perceived the tax to be effective. The respondents who were aware of the perceived health risks of SSBs or those who believed that one should be partly responsible for the health impact of SSBs were more likely to show support for the SSB tax. In adjusted regression models, both one's perceived risk and perceived responsibility of SSBs were positively associated with the perceived effectiveness of the SSB tax after sociodemographic characteristics were controlled. These research findings show evidence that there is public support for implementing an SSB tax to reduce SSB consumption in Taiwan.

Patients' Experiences With Digitalization in the Health Care System: Qualitative Interview Study.

BACKGROUND: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients' digital practices and experiences with digital health services. OBJECTIVE: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them? METHODS: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis. RESULTS: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out. CONCLUSIONS: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Values-Based Medicine Is an Ethical Concept for Implementing the Ethical Principles in Daily Practice.

Values-based medicine (VsBM) is an ethical concept, and bioethical framework has been developed to ensure that medical ethics and values are implemented, pervasive, and powerful parameters influencing decisions about health, clinical practice, teaching, medical industry, career development, malpractice, and research. Neurosurgeons tend to adopt ethics according to their own values and to what they see and learn from teachers. Neurosurgeons, in general, are aware about ethical codes and the patient's rights. However, the philosophy, concept, and principles of medical ethics are rarely included in the training programs or in training courses. The impact of implementing, observing the medical ethics and the patients' value and culture on the course, and outcome of patients' management should not underestimate. The main principles of medical ethics are autonomy, beneficence, nonmaleficence, justice, dignity, and honesty, which should be strictly observed in every step of medical practice, research, teaching, and publication. Evidence-based medicine has been popularized in the last 40-50 years in order to raise up the standard of medical practice. Medical ethics and values have been associated with the medical practice for thousands of years since patients felt a need for treatment. There is no conflict between evidence-based medicine and values-based medicine, as a medical practice should always be performed within a frame of ethics and respect for patients' values. Observing the principles of values-based medicine became very relevant as multicultural societies are dominant in some countries and hospitals in different corners of the world.

Profiles of intuitive eating in adults: the role of self-esteem, interoceptive awareness, and motivation for healthy eating.

OBJECTIVE: Intuitive eating is an eating behavior that has recently come to use mainly in the young population. Knowing that the Lebanese cultural diet differs from other countries, the purpose of this study was to investigate if there is a relationship between self-esteem, interoceptive awareness, and motivation for healthy eating in a sample of Lebanese adults using a Latent Profile Analysis approach. DESIGN: Cross-sectional study. SETTING: Lebanese governorates. PARTICIPANTS: 359 Lebanese participants enrolled in this study (mean age: 22.75 ± 7.04 years, 40.1% males), through convenience sampling in several Lebanese governorates. Participants were asked to fill anonymously the following scales: The Intuitive Eating Scale (IES-2), the Rosenberg Self-Esteem Scale, the Multidimensional Assessment of Interoceptive Awareness Scale (MAIA), and the Motivation for Healthy Eating Scale (MHES). RESULTS: Our findings revealed four profiles: profile 1 (n = 67; 18.66%) characterized by high SE and intermediate interoceptive awareness and motivation for healthy eating; profile 2 (n = 86; 23.97%) presented high SE, interoceptive awareness, and motivation for healthy eating; profile 3 (n = 86; 23.96%) characterized by high SE, interoceptive awareness, and motivation for healthy eating; class 4 (n = 108; 30.08) described by low SE, intermediate interoceptive awareness, and motivation for healthy eating One-way analysis of variance did not observe a significant difference between the four profiles based on intuitive eating (F = 1.810; p = 0.145; ɳp2 = 0.015). CONCLUSIONS: Among a sample of Lebanese people, four profiles of interoceptive awareness, motivation for healthy eating, and self-esteem were observed, with no difference concerning intuitive eating.

Transformer with difference convolutional network for lightweight universal boundary detection.

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer. The TDCN structure consists of three parts: convolution, transformer, and head function. First, a convolution network fused with edge operators is used to extract multiscale difference features. These pixel difference features are then fed to the hierarchical transformer as tokens. Considering the intrinsic characteristics of the boundary detection task, a new boundary-aware self-attention structure was designed in the transformer to provide inductive bias. By incorporating the proposed attention loss function, it introduces the direction of the boundary as strongly supervised information to improve the detection ability of the model. Finally, several head functions with multiscale feature inputs were trained using a bidirectional additive strategy. In the experiments, the proposed method achieved competitive performance on multiple public datasets with fewer model parameters. A single model was obtained to realize universal prediction even for different datasets without retraining, demonstrating the effectiveness of the method. The code is available at https://github.com/neulmc/TDCN.

Artificial intelligence large language model ChatGPT: is it a trustworthy and reliable source of information for sarcoma patients?

Introduction: Since its introduction in November 2022, the artificial intelligence large language model ChatGPT has taken the world by storm. Among other applications it can be used by patients as a source of information on diseases and their treatments. However, little is known about the quality of the sarcoma-related information ChatGPT provides. We therefore aimed at analyzing how sarcoma experts evaluate the quality of ChatGPT's responses on sarcoma-related inquiries and assess the bot's answers in specific evaluation metrics. Methods: The ChatGPT responses to a sample of 25 sarcoma-related questions (5 definitions, 9 general questions, and 11 treatment-related inquiries) were evaluated by 3 independent sarcoma experts. Each response was compared with authoritative resources and international guidelines and graded on 5 different metrics using a 5-point Likert scale: completeness, misleadingness, accuracy, being up-to-date, and appropriateness. This resulted in maximum 25 and minimum 5 points per answer, with higher scores indicating a higher response quality. Scores ≥21 points were rated as very good, between 16 and 20 as good, while scores ≤15 points were classified as poor (11-15) and very poor (≤10). Results: The median score that ChatGPT's answers achieved was 18.3 points (IQR, i.e., Inter-Quartile Range, 12.3-20.3 points). Six answers were classified as very good, 9 as good, while 5 answers each were rated as poor and very poor. The best scores were documented in the evaluation of how appropriate the response was for patients (median, 3.7 points; IQR, 2.5-4.2 points), which were significantly higher compared to the accuracy scores (median, 3.3 points; IQR, 2.0-4.2 points; p = 0.035). ChatGPT fared considerably worse with treatment-related questions, with only 45% of its responses classified as good or very good, compared to general questions (78% of responses good/very good) and definitions (60% of responses good/very good). Discussion: The answers ChatGPT provided on a rare disease, such as sarcoma, were found to be of very inconsistent quality, with some answers being classified as very good and others as very poor. Sarcoma physicians should be aware of the risks of misinformation that ChatGPT poses and advise their patients accordingly.

Resilience-aware MLOps for AI-based medical diagnostic system.

Background: The healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are primarily focused on aspects such as data quality and confidentiality, bias reduction, model deployment, performance monitoring, and continuous improvement. However, so far, MLOps techniques do not take into account the need to provide resilience to disturbances such as adversarial attacks, including fault injections, and drift, including out-of-distribution. This article is concerned with the MLOps methodology that incorporates the steps necessary to increase the resilience of an AI-based medical diagnostic system against various kinds of disruptive influences. Methods: Post-hoc resilience optimization, post-hoc predictive uncertainty calibration, uncertainty monitoring, and graceful degradation are incorporated as additional stages in MLOps. To optimize the resilience of the AI based medical diagnostic system, additional components in the form of adapters and meta-adapters are utilized. These components are fine-tuned during meta-training based on the results of adaptation to synthetic disturbances. Furthermore, an additional model is introduced for post-hoc calibration of predictive uncertainty. This model is trained using both in-distribution and out-of-distribution data to refine predictive confidence during the inference mode. Results: The structure of resilience-aware MLOps for medical diagnostic systems has been proposed. Experimentally confirmed increase of robustness and speed of adaptation for medical image recognition system during several intervals of the system's life cycle due to the use of resilience optimization and uncertainty calibration stages. The experiments were performed on the DermaMNIST dataset, BloodMNIST and PathMNIST. ResNet-18 as a representative of convolutional networks and MedViT-T as a representative of visual transformers are considered. It is worth noting that transformers exhibited lower resilience than convolutional networks, although this observation may be attributed to potential imperfections in the architecture of adapters and meta-adapters. Сonclusion: The main novelty of the suggested resilience-aware MLOps methodology and structure lie in the separating possibilities and activities on creating a basic model for normal operating conditions and ensuring its resilience and trustworthiness. This is significant for the medical applications as the developer of the basic model should devote more time to comprehending medical field and the diagnostic task at hand, rather than specializing in system resilience. Resilience optimization increases robustness to disturbances and speed of adaptation. Calibrated confidences ensure the recognition of a portion of unabsorbed disturbances to mitigate their impact, thereby enhancing trustworthiness.

A Delphi Panel of People With Parkinson's Disease Regarding Responsibility: Toward a Preliminary Taxonomy.

IMPORTANCE: A sense of agency is associated with complex occupation-related responsibilities. A taxonomy can guide clinicians in enhancing responsibility in patients with Parkinson's disease (PwPD). OBJECTIVE: To (1) discover levels of responsibility in occupations for PwPD and (2) propose a taxonomy for occupations. DESIGN: A two-round Delphi study with PwPD and a one-round Delphi study with international experts. SETTING: Electronic survey. PARTICIPANTS: PwPD (N = 75) and international experts (N = 8). OUTCOMES AND MEASURES: PwPD expressed their levels of an inherent sense of responsibility for each occupation (1 = very low responsibility, 5 = very high responsibility). International experts rated their level of agreement (5 = strongly agree, 1 = strongly disagree) with each dimension of the taxonomy. A consensus was determined to have been reached if the interquartile range was ≤1 and 70% agreement in two adjacent categories was achieved. RESULTS: Thirty-three occupation categories were deemed as having very high to moderate responsibility for PwPD. Consequences of actions and the presence of others made up the two-dimensional responsibility taxonomy. Occupations have more challenging responsibility characteristics when they are performed with free choice, a level of high physical effort, alone, and with moral consequences. CONCLUSIONS AND RELEVANCE: This study yielded the first consensus among PwPD regarding responsibility in occupations as well as a classification system for charting the complexity of responsibility in occupations. The occupation list we have created can be beneficial to health care professionals when providing interventions or conducting outcome assessments. Plain-Language Summary: When planning interventions for patients with Parkinson's disease, it can be helpful for clinicians to be aware of patients' perspectives regarding their sense of responsibility to perform occupations. The use of a systematic sequence of challenging occupations with responsibility attributes ranging from less complex to more complex can help enhance patient occupational participation.

The neuroscience of lucid dreaming: Past, present, future.

Lucid dreaming allows conscious awareness and control of vivid dream states; however, its rarity and instability make neuroscientific experimentation challenging. Recent advances in wearable neurotechnology, large-scale collaborations, citizen neuroscience, and artificial intelligence increasingly facilitate the decoding of this intriguing phenomenon.

Sciatica caused by spinal epidural abscess as the initial clinical presentation of colon cancer: a rare case report and review of literature.

BACKGROUND: Colorectal cancer is one of the most frequently diagnosed forms of cancer, and it is associated with several common symptoms and signs such as rectal bleeding, altered bowel habits, abdominal pain, anemia, and unintentional weight loss. Sciatica, a debilitating condition in which the patient experiences paresthesia and pain in the dermatome of associated lumbosacral nerve roots or sciatic nerve distribution, is not considered one of these. Here we present a case of colorectal cancer manifesting symptoms of sciatica alone. CASE PRESENTATION: A 68-year-old male presented with progressive lower back pain radiating to his left thigh and calf over L5/S1 dermatome. Sciatica was suspected and initially underwent conservative treatment with analgesics. However, the symptoms progressed and MRI revealed an epidural abscess surprisingly. Surgical debridement was performed and pus culture isolated Streptococcus gallolyticus. Based on the strong association of S. gallolyticus with colorectal cancer, the presence of this pathogen prompted further tumor evaluation, even in the absence of the typical symptoms and signs. This investigation ultimately leads to the diagnosis of sigmoid adenocarcinoma. CONCLUSIONS: Although rare, sciatica caused by S. gallolyticus infection of the spinal epidural space may serve as the initial presentation of colorectal cancer. Physicians should be aware of the strong association between S. gallolyticus and colorectal cancer. Based on what we currently know about the condition; a thorough systematic assessment of occult neoplasia for patients with S. gallolyticus infection is recommended.

Riboformer: a deep learning framework for predicting context-dependent translation dynamics.

Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences in synonymous codon translation and uncovers a bottleneck in translation elongation. Further, we show that Riboformer can be combined with in silico mutagenesis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.

Assessment and application of non-technical skills in robotic-assisted surgery: a systematic review.

BACKGROUND: Undeniably, robotic-assisted surgery (RAS) has become very popular in recent decades, but it has introduced challenges to the workflow of the surgical team. Non-technical skills (NTS) have received less emphasis than technical skills in training and assessment. The systematic review aimed to update the evidence on the role of NTS in robotic surgery, specifically focusing on evaluating assessment tools and their utilisation in training and surgical education in robotic surgery. METHODS: A systematic literature search of PubMed, PsycINFO, MEDLINE, and EMBASE was conducted to identify primary articles on NTS in RAS. Messick's validity framework and the Modified Medical Education Research Study Quality Instrument were utilised to evaluate the quality of the validity evidence of the abstracted articles. RESULTS: Seventeen studies were eligible for the final analysis. Communication, environmental factors, anticipation and teamwork were key NTS for RAS. Team-related factors such as ambient noise and chatter, inconveniences due to repeated requests during the procedure and constraints due to poor design of the operating room may harm patient safety during RAS. Three novel rater-based scoring systems and one sensor-based method for assessing NTS in RAS were identified. Anticipation by the team to predict and execute the next move before an explicit verbal command improved the surgeon's situational awareness. CONCLUSION: This systematic review highlighted the paucity of reporting on non-technical skills in robotic surgery with only three bespoke objective assessment tools being identified. Communication, environmental factors, anticipation, and teamwork are the key non-technical skills reported in robotic surgery, and further research is required to investigate their benefits to improve patient safety during robotic surgery.

Building digital patient pathways for the management and treatment of multiple sclerosis.

Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient's state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.

The convergence approach may be critical to improving early situational awareness in hostile radioactive environments.

This study explores the impact of a simulated radiological dispersal device (RDD) event in an urban area on young adults around 20 years old. The RDD releases radioactive Cs-137 (7.0E+3 Ci), a common industrial sterilization source. The study aims to demonstrate that combining computational codes and epidemiological models can produce valuable data to guide initial actions when confronting a hostile radioactive environment. The HotSpot Health Physics and RESRAD-RDD codes were used in the simulation to evaluate the event's initial phase. The codes were executed together, and the HotSpot output data was input into RESRAD-RDD. Based on simulated radiation dose levels, estimated doses were incorporated into radioepidemiological models proposed by the Committee on Biological Effects of Ionizing Radiation (BEIR V or VII report). Despite limitations, data transfer between the models revealed no discontinuities or antagonisms. Radiation doses were simulated under three exposure conditions and two atmospheric release modes (day or night), suggesting that atmospheric conditions, sex, and exposure routine can strongly influence the perception of radiation impacts. This combination of methods can increase situational awareness and help with decision-making and developing coping strategies.