Vahid Saidkhani, Kianoosh Bagheri, Ali Khalafi
et al.
Background: Concept mapping and debriefing are educational strategies used to create motivation and meaningful learning. This study compared the effect of teaching incorporating these two techniques on learning and achievement motivation in anesthesia management of neurosurgery among anesthesia students.
Methods: This was a quasi-experimental study involving two experimental groups (concept mapping and debriefing) and one control group. The statistical population included all 5th- and 7th-semester undergraduate students of anesthesia at Ahvaz Jundishapur University of Medical Sciences. Census sampling yielded 51 participants, who were then randomly assigned to three groups: 17 in the concept mapping group, 17 in the debriefing group, and 17 in the control group. The experimental groups were exposed to group concept mapping and debriefing, while the control group received traditional instruction. Data were collected using the Hermans Achievement Motivation Questionnaire and a standard learning questionnaire. Data were analyzed using analysis of covariance (ANCOVA) and t-tests.
Results: Covariance analysis demonstrated that teaching interventions, using both group concept mapping and debriefing, significantly increased achievement motivation and learning outcomes in anesthesia students (p < 0.05). Furthermore, concept mapping yielded a statistically significant increase in achievement motivation as well as meaningful and deep learning compared to debriefing. Regarding learning levels, after two months of intervention, students taught using concept mapping exhibited significantly higher scores (30.41 ± 0.732) than both the debriefing group (29.17 ± 0.772) and the control group (28.78 ± 0.771, p < 0.05).
Conclusion: This research suggests that educational stakeholders should integrate concept mapping into anesthesia curricula, focusing on its motivational components, to significantly boost student achievement and learning outcomes.
Anesthesiology, Medical emergencies. Critical care. Intensive care. First aid
Z. G. Tatarintseva, K. O. Barbuhatti, A. A. Khalafyan
et al.
Background: Lung cancer is the most common primary malignant lung tumor. Coronary artery disease (CAD) is the leading cause of death worldwide. In clinical practice, cases of lung cancer complicated by CAD are encountered, which are associated with a high risk of mortality.Materials and methods: This study analyzed the results of examination, treatment, and follow-up of patients who underwent elective surgery from 01.01.2015 to 01.06.2024. All patients were divided into two groups according to the selected myocardial revascularization method:• Group 1: patients with lung tumors who underwent simultaneous surgery (coronary artery bypass grafting + lung resection) (71 patients);• Group 2: patients with lung tumors who underwent percutaneous coronary intervention with delayed lung surgery (resection or lobectomy) (94 patients).Results: Early in-hospital complications were observed frequently in patients after coronary artery bypass grafting than after percutaneous coronary intervention: acute heart failure – 9.86% vs 0%, p=0.02; atrial fibrillation – 15.4% vs 0%, p=0.013; sepsis – 8.45% vs 0%, p=0.004; post-hypoxic encephalopathy – 9.86% vs 0%, p=0.002).Mortality was observed in the coronary artery bypass grafting group (2 patients) and was not recorded in the percutaneous coronary intervention group; however, did not reach statistically significant difference. During long-term follow-up (6 months to 6 years), mortality due to cancer progression was similarly high in both groups, whereas cardiovascular mortality and ischemic events were comparably low in Groups 1 and 2.Conclusion: The obtained data demonstrate a higher incidence of various complications in the early in-hospital period after coronary artery bypass grafting, which is associated with the greater invasiveness of the method; however, these features do not affect long-term patient survival.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Diseases of the circulatory (Cardiovascular) system
Elaheh Sabziyan Varnousfaderani, Syed A. M. Shihab, Mohammad Taghizadeh
Timely transportation of organs, patients, and medical supplies is critical to modern healthcare, particularly in emergencies and transplant scenarios where even short delays can severely impact outcomes. Traditional ground-based vehicles such as ambulances are often hindered by traffic congestion; while air vehicles such as helicopters are faster but costly. Emerging air vehicles -- Unmanned Aerial Vehicles and electric vertical take-off and landing aircraft -- have lower operating costs, but remain limited by range and susceptibility to weather conditions. A multimodal transportation system that integrates both air and ground vehicles can leverage the strengths of each to enhance overall transportation efficiency. This study introduces a constructive greedy heuristic algorithm for multimodal vehicle dispatching for medical transportation. Four different fleet configurations were tested: (i) ambulances only, (ii) ambulances with Unmanned Aerial Vehicles, (iii) ambulances with electric vertical take-off and landing aircraft, and (iv) a fully integrated fleet of ambulances, Unmanned Aerial Vehicles, and electric vertical take-off and landing aircraft. The algorithm incorporates payload consolidation across compatible routes, accounts for traffic congestion in ground operations and weather conditions in aerial operations, while enabling rapid vehicle dispatching compared to computationally intensive optimization models. Using a common set of conditions, we evaluate all four fleet types to identify the most effective configurations for fulfilling medical transportation needs while minimizing operating costs, recharging/fuel costs, and total transportation time.
Abstract Background Nursing students' practice experiences in the intensive care unit will greatly influence their acquisition of professional skills and the development of their future nursing roles. Aim This study aimed to determine nursing students' first clinical practice experiences in the intensive care unit. Study Design The type of research is qualitative research. The study data were collected from 14 students who were doing clinical practice in the anaesthesia intensive care unit of a university hospital. Face‐to‐face individual interviews were conducted with each student. A semi‐structured interview guide was used in the interviews. Data were analysed with MAXQDA Analytics Pro 2020. This study adhered to the COREQ checklist for reporting. Results Four main themes were reached: ‘First Emotions in the Intensive Care Unit’, ‘Experiences Related to the Perception of Profession’, ‘Experiences Related to Patient Care’ and ‘Experiences Related to Personal Development’. It was determined that the most prominent feelings in these clinical practice experiences of the students were fear in patient care, satisfaction and professional image in professional acquisition, and making life more meaningful in their perspective on life. Conclusions Students evaluated the intensive care clinical practice as beneficial in terms of professional and personal development. It is recommended that an orientation program be organized for students before intensive care clinical practice. Relevance to Clinical Practice Qualified critical care nurses of the future are a product of a qualified nursing clinical education today.
Manual ventilation is an essential skill for healthcare professionals, especially in emergency and resuscitation situations where mechanical ventilation may not be immediately available. However, improper manual ventilation can lead to serious complications such as barotrauma (lung injury caused by excessive pressure), hypoventilation (leading to insufficient oxygenation), hyperventilation (which can cause respiratory alkalosis and reduced cerebral blood flow), and gastric insufflation (which increases the risk of aspiration). This review aimed to analyze the definitions and methods used to assess manual ventilation efficiency in recent studies. A systematic database search was conducted for the period between 2014 and 2023. The primary inclusion criterion was the assessment of manual ventilation quality in adults. Out of 47 identified studies, eight met the inclusion criteria in the review. Most of the reviewed studies focused on key ventilation parameters including tidal volume and ventilation rate, which are critical for ensuring adequate ventilation. However, we found considerable variability in how “effective ventilation” was defined. This review highlights the approach that considers both extrinsic and intrinsic factors as a potentially more comprehensive method for assessing manual ventilation quality. This approach may offer a more consistent and effective framework for ensuring safe and efficient manual ventilation practices.
Medical emergencies. Critical care. Intensive care. First aid
Background. Preoperative gastric ultrasound allows assessing the risks of aspiration and choosing the optimal strategies of anesthetic support. The aim of the study is to establish the diagnostic value of ultrasound examination of gastric contents and volume in patients with acute coronary syndrome undergoing primary percutaneous coronary intervention, using the results of our own studies; to review the literature on preoperative fasting in adults and children. Materials and methods. The research included 12 patients with acute coronary syndrome who underwent primary percutaneous coronary intervention with anesthetic support. The time of the last meal and fluid intake was noted, and ultrasound imaging of the gastric antrum was performed. Results. Eleven patients reported the time of preoperative fasting; one patient was admitted in a medication-induced sleep after resuscitation and return of spontaneous circulation, and the information about the last solid food and fluid intake was not available. Four patients were classified as grade 0 (three cases) and grade 1 (one case) on ultrasound diagnosis, which corresponded to a minimal and ≤ 1.5 ml/kg (≤ 100 ml) gastric contents at the level of baseline gastric secretion or a minimal residual volume of clear fluid drunk and, consequently, a low risk of aspiration. One patient was classified as grade 2 with a cross-sectional area of 14 cm2, which corresponded to 142 ml of fluid for age and indicated a large gastric volume (> 1.5 ml/kg) and a high risk of aspiration. In 7 patients, a full stomach with thick liquid/solid food and high risks of regurgitation and aspiration were diagnosed; of these, 3 people had an early phase after solid food intake, and 4 individuals (including a patient with unknown nutritional status) had a late phase. The ultrasound results were consistent with and complemented the information regarding food and fluid intake. Conclusions. The study demonstrated the diagnostic value of gastric ultrasound as a non-invasive method for assessing the contents, volume and phase of emptying in patients with acute coronary syndrome undergoing primary percutaneous coronary intervention.
Medical emergencies. Critical care. Intensive care. First aid
Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. Thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF.
Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capable of reasoning, interacting with tools, and adapting to complex tasks. While LLM-based agentic systems have shown promise across many domains, their application to medical imaging remains in its infancy. In this work, we introduce AURA, the first visual linguistic explainability agent designed specifically for comprehensive analysis, explanation, and evaluation of medical images. By enabling dynamic interactions, contextual explanations, and hypothesis testing, AURA represents a significant advancement toward more transparent, adaptable, and clinically aligned AI systems. We highlight the promise of agentic AI in transforming medical image analysis from static predictions to interactive decision support. Leveraging Qwen-32B, an LLM-based architecture, AURA integrates a modular toolbox comprising: (i) a segmentation suite with phase grounding, pathology segmentation, and anatomy segmentation to localize clinically meaningful regions; (ii) a counterfactual image-generation module that supports reasoning through image-level explanations; and (iii) a set of evaluation tools including pixel-wise difference-map analysis, classification, and advanced state-of-the-art components to assess diagnostic relevance and visual interpretability.
Lemar Abdi, Francisco Caetano, Amaan Valiuddin
et al.
In medical imaging, unsupervised out-of-distribution (OOD) detection offers an attractive approach for identifying pathological cases with extremely low incidence rates. In contrast to supervised methods, OOD-based approaches function without labels and are inherently robust to data imbalances. Current generative approaches often rely on likelihood estimation or reconstruction error, but these methods can be computationally expensive, unreliable, and require retraining if the inlier data changes. These limitations hinder their ability to distinguish nominal from anomalous inputs efficiently, consistently, and robustly. We propose a reconstruction-free OOD detection method that leverages the forward diffusion trajectories of a Stein score-based denoising diffusion model (SBDDM). By capturing trajectory curvature via the estimated Stein score, our approach enables accurate anomaly scoring with only five diffusion steps. A single SBDDM pre-trained on a large, semantically aligned medical dataset generalizes effectively across multiple Near-OOD and Far-OOD benchmarks, achieving state-of-the-art performance while drastically reducing computational cost during inference. Compared to existing methods, SBDDM achieves a relative improvement of up to 10.43% and 18.10% for Near-OOD and Far-OOD detection, making it a practical building block for real-time, reliable computer-aided diagnosis.
Mental health risk is a critical global public health challenge, necessitating innovative and reliable assessment methods. With the development of large language models (LLMs), they stand out to be a promising tool for explainable mental health care applications. Nevertheless, existing approaches predominantly rely on subjective textual mental records, which can be distorted by inherent mental uncertainties, leading to inconsistent and unreliable predictions. To address these limitations, this paper introduces ProMind-LLM. We investigate an innovative approach integrating objective behavior data as complementary information alongside subjective mental records for robust mental health risk assessment. Specifically, ProMind-LLM incorporates a comprehensive pipeline that includes domain-specific pretraining to tailor the LLM for mental health contexts, a self-refine mechanism to optimize the processing of numerical behavioral data, and causal chain-of-thought reasoning to enhance the reliability and interpretability of its predictions. Evaluations of two real-world datasets, PMData and Globem, demonstrate the effectiveness of our proposed methods, achieving substantial improvements over general LLMs. We anticipate that ProMind-LLM will pave the way for more dependable, interpretable, and scalable mental health case solutions.
This paper introduces the Shepherd Test, a new conceptual test for assessing the moral and relational dimensions of superintelligent artificial agents. The test is inspired by human interactions with animals, where ethical considerations about care, manipulation, and consumption arise in contexts of asymmetric power and self-preservation. We argue that AI crosses an important, and potentially dangerous, threshold of intelligence when it exhibits the ability to manipulate, nurture, and instrumentally use less intelligent agents, while also managing its own survival and expansion goals. This includes the ability to weigh moral trade-offs between self-interest and the well-being of subordinate agents. The Shepherd Test thus challenges traditional AI evaluation paradigms by emphasizing moral agency, hierarchical behavior, and complex decision-making under existential stakes. We argue that this shift is critical for advancing AI governance, particularly as AI systems become increasingly integrated into multi-agent environments. We conclude by identifying key research directions, including the development of simulation environments for testing moral behavior in AI, and the formalization of ethical manipulation within multi-agent systems.
Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin
et al.
Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in clinical practice, the ability to accurately determine relative positions on medical images is a fundamental prerequisite. Despite its importance, this capability remains highly underexplored. To address this gap, we evaluate the ability of state-of-the-art VLMs, GPT-4o, Llama3.2, Pixtral, and JanusPro, and find that all models fail at this fundamental task. Inspired by successful approaches in computer vision, we investigate whether visual prompts, such as alphanumeric or colored markers placed on anatomical structures, can enhance performance. While these markers provide moderate improvements, results remain significantly lower on medical images compared to observations made on natural images. Our evaluations suggest that, in medical imaging, VLMs rely more on prior anatomical knowledge than on actual image content for answering relative position questions, often leading to incorrect conclusions. To facilitate further research in this area, we introduce the MIRP , Medical Imaging Relative Positioning, benchmark dataset, designed to systematically evaluate the capability to identify relative positions in medical images.
The research evaluates lightweight medical abstract classification methods to establish their maximum performance capabilities under financial budget restrictions. On the public medical abstracts corpus, we finetune BERT base and Distil BERT with three objectives cross entropy (CE), class weighted CE, and focal loss under identical tokenization, sequence length, optimizer, and schedule. DistilBERT with plain CE gives the strongest raw argmax trade off, while a post hoc operating point selection (validation calibrated, classwise thresholds) sub stantially improves deployed performance; under this tuned regime, focal benefits most. We report Accuracy, Macro F1, and WeightedF1, release evaluation artifacts, and include confusion analyses to clarify error structure. The practical takeaway is to start with a compact encoder and CE, then add lightweight calibration or thresholding when deployment requires higher macro balance.
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.
Objective: In the management of haemodynamically unstable patients, cardiac output (CO) measurement provides clinicians with important data on organ tissue perfusion. This measurement can be performed by pulse-induced contour cardiac output (PiCCO) using thermodilution method, which is a less invasive method, and ultrasonic cardiac output monitoring (USCOM), which is completely non-invasive. The aim of this study was to investigate the clinical relevance of CO and cardiac index measurements obtained by USCOM in patient’s with sepsis and septic shock by comparing them with the PiCCO technique, which has been used as a reference measurement method in recent years.
Materials and Methods: In this prospective study, 36 patient’s with sepsis and septic shock ventilated with 8-10 mL/kg tidal volume without respiratory effort were included. Patient’s with arrhythmia, known heart failure or pulmonary embolism were excluded.
Results: After averaging the PiCCO and USCOM measurements performed by different clinicians, the heart rate was found to be 3.23 L/min/m2 with PiCCO and 2.24 L/min/m2 with USCOM. When the two results were compared, the difference was statistically significant (p=0.01). Stroke volume variation was 15.80% with PiCCO and 52.89% with USCOM. When the two results were compared, the difference was statistically significant (p=0.01).
Conclusion: There was no agreement between USCOM and PiCCO measurements in sepsis patient’s. In our opinion, more studies are needed for USCOM reliability.
E.M. Khoroshun, V.V. Makarov, V.V. Nehoduiko
et al.
Background. The purpose is to investigate metal non-opaque foreign bodies of firearm origin. Materials and methods. Five cases of removing soft tissue foreign bodies of gunshot origin were investigated, when radiography of the limb soft tissues didn’t detect foreign bodies, but they were removed during the primary surgical wound debridement. X-ray of soft tissue gunshot wounds found no foreign bodies. To study radiographic density, foreign bodies were placed in a foam model with subsequent multislice computed tomography. For spectral analysis of metal foreign bodies, we used wavelengths unconventional for X-ray, which allowed to conduct X-ray fluorescence and X-ray structural studies. Results. Radiographic density of metal foreign bodies ranged from 989 to 2123 HU. Given the different thickness of foreign bodies, from 0.4 to 3.2 mm, the average was 1700 ± 189 HU. The foam had radiographic density of –969 HU with model dimensions of 200 × 100 × 50 mm. When examining samples of foreign bodies, it was found that they are deformed, have different thicknesses. One of the samples is light and smooth on one side, and dark and rough on the other. X-ray fluorescence results: composition on the light side (% by mass): Al — base, Mn — 0.8, Fe — 0.3, Zn — 0.1, Cr — 0.05, Ti — 0.2, corresponding to Al-Mn alloy. The dark side is an oxidized Al-Mn alloy. In the spectrum of the dark side of the sample, the Br-Kα line was detected, which indicates the participation of bromine compounds in the oxidation process. From the smooth side of the sample, the spectrum of this line is not determined. Conclusions. Non-opaque metal foreign bodies of gunshot origin are a rare phenomenon. Metal foreign bodies with low radiographic density are non-ferromagnetic, the use of modern magnetic surgical instruments will not be effective. Visualization of metal foreign bodies, which are not determined by radiography, is possible with the help of multislice computed tomography. The use of the wavelength of primary radiation, which is unconventional for X-ray spectral analysis, and original X-ray optical schemes allows for quantitative determination of the composition and structure of any metal foreign bodies.
Medical emergencies. Critical care. Intensive care. First aid