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The aim of this study was to assess the clinical and radiographic outcomes after radioscapholunate (RSL) fusion for posttraumatic osteoarthritis. This was a retrospective, dual-center study of all patients who underwent RSL fusion between 1995 and 2015 for posttraumatic radiocarpal osteoarthritis. Patients were assessed at the final review to determine clinical (pain, wrist range of motion and strength), self-reported (QuickDASH, PRWE and MWS scores) and radiological (degenerative osteoarthritis in the scaphotrapeziotrapezoid (STT) or midcarpal joint and radiocarpal fusion) outcomes. We analyzed three groups: RSL fusion alone, RSL fusion with distal scaphoid excision (DSE) and RSL fusion with DSE and triquetrum excision (TE). Eighty-five patients were included; 10 were lost to follow-up and 11 required conversion to total wrist fusion before the final review. Finally, 64 patients had both clinical and radiographic evaluations. The mean follow-up was 9.1 years (range 1-21.4). RSL fusion alone was performed in 29 patients, RSL fusion with DSE in 23 and RSL fusion with DSE and TE in 12. At the final follow-up, the three groups did not differ in their pain or wrist motion. Overall, 47 (73%) patients were satisfied or very satisfied with the procedure. DSE significantly decreased STT osteoarthritis and radiocarpal non-union. The total wrist osteoarthritis rate after RSL fusion was 55%. RSL fusion is an effective procedure to preserve some motion in wrists with posttraumatic radiocarpal osteoarthritis. DSE prevents STT osteoarthritis by removing bony impingement and increases the fusion rate. LEVEL OF EVIDENCE: Level IV, Case series, Therapeutic studies.

International audience; The typical Internet user has data spread over several devices and across several online systems. We demonstrate an open-source system for integrating user's data from different sources into a single Knowledge Base. Our system integrates data of different kinds into a coherent whole, starting with email messages, calendar, contacts, and location history. It is able to detect event periods in the user's location data and align them with calendar events. We will demonstrate how to query the system within and across different dimensions, and perform analytics over emails, events, and locations.

International audience; We designed a system to infer multimodal itineraries traveled by a user from a combination of smartphone sensor data (e.g., GPS, Wi-Fi, accelerometer) and knowledge of the transport network infrastructure (e.g., road and rail maps, public transportation timetables). The system uses a Transportation network that captures the set of possible paths of this network for the modes, e.g., foot, bicycle, road_vehicle, and rail. This Transportation network is constructed from OpenStreetMap data and public transportation routes published online by transportation agencies in GTFS format. Thesystem infers itineraries from a sequence of smartphone observations in two phases. The first phase uses a dynamic Bayesian network that models the probabilistic relationship between paths in Transportation network and sensor data. The second phase attempts to match portions recognized as road_vehicle or rail with possible public transportation routes of type bus, train, metro, or tram extracted from the GTFS source. We evaluated the performance of our systemwith data from users traveling over the Paris area who were asked to record data for different trips via an Android application. Itineraries were annotated with modes and public transportation routes taken and we report on the results of the recognition.

Thèse confidentielle; Non disponible