Waltonbjerring6450

The clinical spectrum of injuries in crevasse accidents can range from benign to life-threatening, even including death. To date, little is known about incidence and causes.

We retrospectively analyzed mountain rescue missions that included crevasse accidents and took place in Switzerland from 2010 to 2020. Demographic and epidemiological data were collected. Injury severity was graded according to the National Advisory Committee for Aeronautics (NACA) score. Winter season was defined as December to May, and summer season as June to November.

A total of 321 victims of crevasse falls were included in the study. The median age of victims was 41.2 years (interqauartile range [IQR] 31.3 to 51.6), with 82% (n=260) being male and 59% (n=186) foreigners. The typical altitude range at which rescue missions were performed was between 3000 and 3499m (44% of all cases). The median depth of the fall was 15 meters (IQR 8 to 20) during the winter season compared to 8 meters (IQR 5 to 10) during the summer, p<0.001. Overall mortality was 6.5%. The NACA score was ≥4 for 9.4% (n=30) of the victims. 55% (n=177) had a NACA score of 0 or 1. There was a significant positive correlation between the depth of fall and the injury severity (Pearson`s correlation r=0.35, 95%- confidence interval 0.18 to 0.51), p<0.001.

More than half of victims fallen into a crevasse are uninjured or sustain mild injury. Life-threathening injuries were found in about 10% of victims and the crevasse fall was fatal in 6.5% of cases. Injury severity positively correlates with the depth of fall, which is higher during winter season.

More than half of victims fallen into a crevasse are uninjured or sustain mild injury. Life-threathening injuries were found in about 10% of victims and the crevasse fall was fatal in 6.5% of cases. Injury severity positively correlates with the depth of fall, which is higher during winter season.

Fracture-related infection (FRI) remains one of the most challenging complications in orthopaedic trauma surgery. An early diagnosis is of paramount importance to guide treatment. The primary aim of this study was to compare the Centers for Disease Control and Prevention (CDC) criteria for the diagnosis of organ/space surgical site infection (SSI) to the recently developed diagnostic criteria of the FRI consensus definition in operatively treated fracture patients.

This international multicenter retrospective cohort study evaluated 257 patients with 261 infections after operative fracture treatment. All patients included in this study were considered to have an FRI and treated accordingly ('intention to treat'). The minimum follow-up was one year. Infections were scored according to the CDC criteria for organ/space SSI and the diagnostic criteria of the FRI consensus definition.

Overall, 130 (49.8%) FRIs were captured when applying the CDC criteria for organ/space SSI, whereas 258 (98.9%) FRIs were captI consensus definition. When applying these diagnostic criteria, 98.9% of the infections that occured after operative fracture treatment could be captured. The CDC criteria for organ/space SSI captured less than half of the patients with an FRI requiring treatment, and seemed to have less diagnostic value in this patient population.In the latest years, the healthcare domain has seen an increasing interest in the definition of intelligent systems to support clinicians in their everyday tasks and activities. Among others, also the field of Evidence-Based Medicine is impacted by this twist, with the aim to combine the reasoning frameworks proposed thus far in the field with mining algorithms to extract structured information from clinical trials, clinical guidelines, and Electronic Health Records. In this paper, we go beyond the state of the art by proposing a new end-to-end pipeline to address argumentative outcome analysis on clinical trials. More precisely, our pipeline is composed of (i) an Argument Mining module to extract and classify argumentative components (i.e., evidence and claims of the trial) and their relations (i.e., support, attack), and (ii) an outcome analysis module to identify and classify the effects (i.e., improved, increased, decreased, no difference, no occurrence) of an intervention on the outcome of the trial, based on PICO elements. We annotated a dataset composed of more than 500 abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database, leading to a labeled dataset with 4198 argument components, 2601 argument relations, and 3351 outcomes on five different diseases (i.e., neoplasm, glaucoma, hepatitis, diabetes, hypertension). We experiment with deep bidirectional transformers in combination with different neural architectures (i.e., LSTM, GRU and CRF) and obtain a macro F1-score of.87 for component detection and.68 for relation prediction, outperforming current state-of-the-art end-to-end Argument Mining systems, and a macro F1-score of.80 for outcome classification.Resembling the role of disease diagnosis in Western medicine, pathogenesis (also called Bing Ji) diagnosis is one of the utmost important tasks in traditional Chinese medicine (TCM). In TCM theory, pathogenesis is a complex system composed of a group of interrelated factors, which is highly consistent with the character of systems science (SS). In this paper, we introduce a heuristic definition called pathogenesis network (PN) to represent pathogenesis in the form of the directed graph. Accordingly, a computational method of pathogenesis diagnosis, called network differentiation (ND), is proposed by integrating the holism principle in SS. ND consists of three stages. The first stage is to generate all possible diagnoses by Cartesian Product operated on specified prior knowledge corresponding to the input symptoms. The second stage is to screen the validated diagnoses by holism principle. The third stage is to pick out the clinical diagnosis by physician-computer interaction. Some theorems are stated and proved for the further optimization of ND in this paper. We conducted simulation experiments on 100 clinical cases. The experimental results show that our proposed method has an excellent capability to fit the holistic thinking in the process of physician inference.Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance during sleep, which determines episodes of partial or complete interruption of airflow. The detection and treatment of OSAS is particularly important in patients who suffered a stroke, because the presence of severe OSAS is associated with higher mortality, worse neurological deficits, worse functional outcome after rehabilitation, and a higher likelihood of uncontrolled hypertension. The gold standard test for diagnosing OSAS is polysomnography (PSG). Unfortunately, performing a PSG in an electrically hostile environment, like a stroke unit, on neurologically impaired patients is a difficult task; moreover, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. Hence, a simple and automated recognition system to identify OSAS cases among acute stroke patients, relying on routinely recorded vital signs, is highly desirable. The vast majority of the work done so far focuses on data recorded in ideal conditions and highly selected patients, and thus it is hardly exploitable in real-life circumstances, where it would be of actual use. In this paper, we propose a novel convolutional deep learning architecture able to effectively reduce the temporal resolution of raw waveform data, like physiological signals, extracting key features that can be used for further processing. We exploit models based on such an architecture to detect OSAS events in stroke unit recordings obtained from the monitoring of unselected patients. Unlike existing approaches, annotations are performed at one-second granularity, allowing physicians to better interpret the model outcome. Results are considered to be satisfactory by the domain experts. Moreover, through tests run on a widely-used public OSAS dataset, we show that the proposed approach outperforms current state-of-the-art solutions.Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. check details The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis.Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to (i) reduce the irrelevant messages by identifying and focusing the analysis only on indi allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.Leukocytes are key cellular elements of the innate immune system in all vertebrates, which play a crucial role in defending organisms against invading pathogens. Tracking these highly migratory and amorphous cells in in vivo models such as zebrafish embryos is a challenging task in cellular immunology. As temporal and special analysis of these imaging datasets by a human operator is quite laborious, developing an automated cell tracking method is highly in demand. Despite the remarkable advances in cell detection, this field still lacks powerful algorithms to accurately associate the detected cell across time frames. The cell association challenge is mostly related to the amorphous nature of cells, and their complicated motion profile through their migratory paths. To tackle the cell association challenge, we proposed a novel deep-learning-based object linkage method. For this aim, we trained the 3D cell association learning network (3D-CALN) with enough manually labelled paired 3D images of single fluorescent zebrafish's neutrophils from two consecutive frames.