Digital health in musculoskeletal care: where are we heading?

BMC Musculoskeletal Disorders launched a Collection on digital health to get a sense of where the wind is blowing, and what impact these technologies are and will have on musculoskeletal medicine. This editorial summarizes findings and focuses on some key topics, which are valuable as digital health establishes itself in patient care. Elements discussed are digital tools for the diagnosis, prognosis and evaluation of rheumatic and musculoskeletal diseases, coupled together with advances in methodologies to analyse health records and imaging. Moreover, the acceptability and validity of these digital advances is discussed. In sum, this editorial and the papers presented in this article collection on Digital health in musculoskeletal care will give the interested reader both a glance towards which future we are heading, and which new challenges these advances bring

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Introduction Treating severely injured patients requires numerous critical decisions within short intervals in a highly complex situation. The coordination of a trauma team in this setting has been shown to be associated with multiple procedural errors, even of experienced care teams. Machine learning (ML) is an approach that estimates outcomes based on past experiences and data patterns using a computer-generated algorithm. This systematic review aimed to summarize the existing literature on the value of ML for the initial management of severely injured patients. Methods We conducted a systematic review of the literature with the goal of finding all articles describing the use of ML systems in the context of acute management of severely injured patients. MESH search of Pubmed/Medline and Web of Science was conducted. Studies including fewer than 10 patients were excluded. Studies were divided into the following main prediction groups: (1) injury pattern, (2) hemorrhage/need for transfusion, (3) emergency intervention, (4) ICU/length of hospital stay, and (5) mortality. Results Thirty-six articles met the inclusion criteria; among these were two prospective and thirty-four retrospective case series. Publication dates ranged from 2000 to 2020 and included 32 different first authors. A total of 18,586,929 patients were included in the prediction models. Mortality was the most represented main prediction group (n = 19). ML models used were artificial neural network ( n = 15), singular vector machine (n = 3), Bayesian network (n = 7), random forest (n = 6), natural language processing (n = 2), stacked ensemble classifier [SuperLearner (SL), n = 3], k-nearest neighbor (n = 1), belief system (n = 1), and sequential minimal optimization (n = 2) models. Thirty articles assessed results as positive, five showed moderate results, and one article described negative results to their implementation of the respective prediction model. Conclusions While the majority of articles show a generally positive result with high accuracy and precision, there are several requirements that need to be met to make the implementation of such models in daily clinical work possible. Furthermore, experience in dealing with on-site implementation and more clinical trials are necessary before the implementation of ML techniques in clinical care can become a reality

Analysis of the distribution of uranium and thorium in the territory of Yurga according to the data of studying the snow cover

In work are presented schematic maps of the spatial distribution of the contents of uranium and thorium from the atmosphere on the snow cover of the territory of Yurga with characteristics of possible sources. The highest total load and the coefficients of the relative increase in the total load of radioactive elements are in the industrial zone and the private sector, from where the contamination goes to other functional areas

COVID-19 measures as an opportunity to reduce the environmental footprint in orthopaedic and trauma surgery

BackgroundClimate change and its consequences on our everyday life have also tremendous impacts on public health and the health of each individual. The healthcare sector currently accounts for 4.4% of global greenhouse gas emissions. The share of the emissions in the health care system caused by the transportation sector is 7%. The study analyses the effect of video consultation on the CO2 emissions during the Covid-19 pandemic in an outpatient clinic of the department of orthopaedics and traumatology surgery at a German university hospital.MethodsThe study participants were patients who obtained a video consultation in the period from June to December 2020 and voluntarily completed a questionnaire after the consultation. The type of transport, travel time and waiting time as well as patient satisfaction were recorded by questionnaire.ResultsThe study comprised 51 consultations. About 70% of respondents would have travelled to the clinic by car. The reduction in greenhouse gas emissions of video consultations compared to a face-to-face presentation was 97% in our model investigation.ConclusionThe video consultation can be a very important part of the reduction of greenhouse gas emissions in the health care system. It also saves time for the doctor and patient and can form an essential part of individual patient care

Анализ эффективности применения технологий по выравниванию профиля приемистости при разработке нефтяных месторождений Западной Сибири

В работе рассматриваются технологии выравнивания профиля приемистости и анализ их эффективности на примере нефтяных месторождений находящихся на последней стадии разработки.The paper discusses the technologies for leveling the injectivity profile and the analysis of their effectiveness on the example of oil fields at the last stage of development

Effect of surgical experience and spine subspecialty on the reliability of the {AO} Spine Upper Cervical Injury Classification System

OBJECTIVE The objective of this paper was to determine the interobserver reliability and intraobserver reproducibility of the AO Spine Upper Cervical Injury Classification System based on surgeon experience (< 5 years, 5–10 years, 10–20 years, and > 20 years) and surgical subspecialty (orthopedic spine surgery, neurosurgery, and "other" surgery). METHODS A total of 11,601 assessments of upper cervical spine injuries were evaluated based on the AO Spine Upper Cervical Injury Classification System. Reliability and reproducibility scores were obtained twice, with a 3-week time interval. Descriptive statistics were utilized to examine the percentage of accurately classified injuries, and Pearson’s chi-square or Fisher’s exact test was used to screen for potentially relevant differences between study participants. Kappa coefficients (κ) determined the interobserver reliability and intraobserver reproducibility. RESULTS The intraobserver reproducibility was substantial for surgeon experience level (< 5 years: 0.74 vs 5–10 years: 0.69 vs 10–20 years: 0.69 vs > 20 years: 0.70) and surgical subspecialty (orthopedic spine: 0.71 vs neurosurgery: 0.69 vs other: 0.68). Furthermore, the interobserver reliability was substantial for all surgical experience groups on assessment 1 (< 5 years: 0.67 vs 5–10 years: 0.62 vs 10–20 years: 0.61 vs > 20 years: 0.62), and only surgeons with > 20 years of experience did not have substantial reliability on assessment 2 (< 5 years: 0.62 vs 5–10 years: 0.61 vs 10–20 years: 0.61 vs > 20 years: 0.59). Orthopedic spine surgeons and neurosurgeons had substantial intraobserver reproducibility on both assessment 1 (0.64 vs 0.63) and assessment 2 (0.62 vs 0.63), while other surgeons had moderate reliability on assessment 1 (0.43) and fair reliability on assessment 2 (0.36). CONCLUSIONS The international reliability and reproducibility scores for the AO Spine Upper Cervical Injury Classification System demonstrated substantial intraobserver reproducibility and interobserver reliability regardless of surgical experience and spine subspecialty. These results support the global application of this classification system