Search

The impact of fulfillment of patient expectations throughout recovery on satisfaction in total knee arthroplasty (TKA) is not well understood. Utilizing a standardized TKA method with a robotically assisted (RA) platform, we investigated the impact of expectation fulfillment at 3-month (M) and 6-M on 1-year (Y) and 2-Y satisfaction. We hypothesize that early fulfillment of patient expectations is associated with improved midterm patient satisfaction. We also compare improvements in outcomes with RA-TKA to those of a recent large national TKA cohort study to determine whether RA-TKA meets or exceeds the current standard of care. One hundred six patients were prospectively enrolled and underwent robotic-assisted TKA by a single surgeon using the OMNIBotics system. Patients completed KOOS and New Knee Society Score (KSS) preoperatively and at 3M, 6M, 1Y, and 2Y postoperatively. Expectation fulfillment was subdivided into patients who were below or above the average expectation score pre-operatively, or fulfillment of expectation score at 3M and 6M post TKA. The satisfaction of each group was then compared at 1Y and 2Y post TKA. Study cohort outcomes were compared to contemporary literature from the Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement (FORCE–TJR) database. Patients with greater than average KSS expectation fulfillment at 3M reported significantly higher KSS satisfaction scores at 1Y (34.9 ± 5.3 vs 30.6 ± 6.7, p = 0.0012), and patients with greater than average 6M expectation fulfillment reported improved satisfaction at 1Y (33.9 ± 5.9 vs 31.1 ± 6.7, p = 0.0330) and 2Y (35.2 ± 4.8 vs 32.4 ± 6.8, p = 0.0323). When compared to the FORCE-TJR database, no significant differences in post-operative KOOS outcomes were observed except for Sports and Recreation, for which the RAS group demonstrated higher scores (65.5 ± 30.0 vs 53.0 ± 27.1 p

Accurate and topical spatial datasets representing human populations are foundational to solving humanitarian issues. This paper provides unique solutions to accurately map human settlements both at scale and across remote areas. A method of village boundary extraction from very high resolution optical satellite imagery is proposed. Furthermore, the supplement of a crowd-sourced validation process to filter the detections for higher accuracy and the automated mosaic techniques are detailed. To demonstrate the computational and informational scalability of the process, four distinct geographic locations in Nigeria, Somalia, Pakistan, and Afghanistan were analyzed for a total processed area of 592 000 km 2 , comprised of 1159 high-resolution DigitalGlobe images. The geographic variability of the locations and the scale of the projects required dynamic and automated solutions, respectively. The curated results exhibit high recall and precision of human settlement data in remote as well as urban areas. Crowdsourced validation allows complete control over the precision of the final village boundary layer, and given time, an effective 100% precision can be achieved. This highly scalable and precise system is perfectly adequate for processing regional and country-scale areas, with minimal human effort.