Retrospective analysis of treatment decisions and clinical outcome of Lisfranc injuries: operative vs. conservative treatment

Purpose: Lisfranc injuries are rare and often pose a challenge for surgeons, particularly in initially missed or neglected cases. The evidence on which subtypes of Lisfranc injuries are suitable for conservative treatment or should undergo surgery is low. The aim of this study was to retrospectively analyze treatment decisions of Lisfranc injuries and the clinical outcome of these patients within the last ten years. Methods: All patients treated due to a Lisfranc injury in a German level I trauma centre from January 2011 until December 2020 were included in this study. Radiologic images and medical data from the patient files were analyzed concerning the classification of injury, specific radiologic variables, such as the Buehren criteria, patient baseline characteristics, and patient outcome reported with the Foot Function Index (FFI). Results: Ninety-nine patients were included in this study (conservative = 20, operative = 79). The overall clinical outcome assessed by the FFI was good (FFI sum 23.93, SD 24.93); patients that were identified as suitable for conservative treatment did not show inferior functional results. Qualitative radiological factors like the grade of displacement and the trauma mechanism were more strongly associated with the decision for surgical treatment than quantitative radiologic factors such as the distance from the first to the second metatarsal bone. Conclusion: If the indication for conservative or operative treatment of Lisfranc injuries is determined correctly, the clinical outcome can be comparable. These decisions should be based on several factors including quantitative and qualitative radiologic criteria, as well as the trauma mechanism

Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.Peer Reviewe

Robotic observation pipeline for small bodies in the solar system based on open-source software and commercially available telescope hardware

The observation of small bodies in the Space Environment is an ongoing important task in astronomy. While nowadays new objects are mostly detected in larger sky surveys, several follow-up observations are usually needed for each object to improve the accuracy of orbit determination. In particular objects orbiting close to Earth, so called Near-Earth Objects (NEOs) are of special concern as a small but not negligible fraction of them can have a non-zero impact probability with Earth. Additionally, the observation of manmade space debris and tracking of satellites falls in the same class measurements. Telescopes for these follow-up observations are mainly in a aperture class between 1 m down to approximately 25 cm. These telescopes are often hosted by amateur observatories or dedicated companies like 6ROADS specialized on this type of observation. With upcoming new NEO search campaigns by very wide field of view telescopes, like the Vera C. Rubin Observatory, NASA’s NEO surveyor space mission and ESA’s Flyeye telescopes, the number of NEO discoveries will increase dramatically. This will require an increasing number of useful telescopes for follow-up observations at different geographical locations. While well-equipped amateur astronomers often host instruments which might be capable of creating useful measurements, both observation planning and scheduling, and also analysis are still a major challenge for many observers. In this work we present a fully robotic planning, scheduling and observation pipeline that extends the widely used open-source cross-platform software KStars/Ekos for Instrument Neutral Distributed Interface (INDI) devices. The method consists of algorithms which automatically select NEO candidates with priority according to ESA’s Near-Earth Object Coordination Centre (NEOCC). It then analyses detectable objects (based on limiting magnitudes, geographical position, and time) with preliminary ephemeris from the Minor Planet Center (MPC). Optimal observing slots during the night are calculated and scheduled. Immediately before the measurement the accurate position of the minor body is recalculated and finally the images are taken. Besides the detailed description of all components, we will show a complete robotic hard- and software solution based on our methods.TS-R acknowledges funding from the NEO-MAPP project (H2020-EU-2-1-6/870377). This work was (partially) funded by the Spanish MICIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” by the “European Union” through grant RTI2018-095076-B-C21, and the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia “María de Maeztu”) through grant CEX2019-000918-M

Global Carbon Budget 2022

Accurate assessment of anthropogenic carbon dioxide (CO$_2$) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO$_2$ emissions (E$_{FOS}$) are based on energy statistics and cement production data, while emissions from land-use change (E$_{LUC}$), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO$_2$ concentration is measured directly, and its growth rate (G$_{ATM}$) is computed from the annual changes in concentration. The ocean CO$_2$ sink (S$_{OCEAN}$) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO$_2$ sink (S$_{LAND}$) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B$_{IM}$), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, E$_{FOS}$ increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr$^{−1}$ (9.9 ± 0.5 GtC yr$^{−1}$ when the cement carbonation sink is included), and E$_{LUC}$ was 1.1 ± 0.7 GtC yr$^{−1}$, for a total anthropogenic CO$_2$ emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr$^{−1}$ (40.0 ± 2.9 GtCO$_2$). Also, for 2021, G$_{ATM}$ was 5.2 ± 0.2 GtC yr$^{−1}$ (2.5 ± 0.1 ppm yr$^{−1}$), S$_{OCEAN}$ was 2.9  ± 0.4 GtC yr$^{−1}$, and S$_{LAND}$ was 3.5 ± 0.9 GtC yr$^{−1}$, with a B$_{IM}$ of −0.6 GtC yr$^{−1}$ (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO$_2$ concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in E$_{FOS}$ relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO$_2$ concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr$^{−1}$ persist for the representation of annual to semi-decadal variability in CO$_2$ fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO$_2$ flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b)