Introduction

Bracing, a strategy employed by humans and robotic devices, can be generally described as a parallel mechanical link between the actor, the environment, and/or the workpiece that alters the mechanical impedance between the tool and workpiece in order to improve task performance. In this study we investigated the potential value of bracing in the context of bone milling to treat cam-type femoroacetabular impingement (FAI) lesions. The goal of this study was to evaluate whether a proposed bracing technique could enable a user to perform a cam resection more accurately and quickly than a currently employed arthroscopic technique.

Although wait-times for hip fracture surgery have been linked to mortality and are being used as quality-of-care indicators worldwide, controversy exists about the duration of the wait that leads to complications. Our objective was to use new population-based wait-time data to emprically derive an optimal time window in which to conduct hip fracture surgery before the risk of complications increases. We used health administrative data from Ontario, Canada to identify hip fracture patients between 2009 and 2014. The main exposure was the time from hospital arrival to surgery (in hours). The primary outcome was mortality within 30 days. Secondary outcomes included a composite of mortality or other medical complications (MI, DVT, PE, and pneumonia) also within 30 days. Risk-adjusted cubic splines modeled the probability of each complication according to wait-time. The inflection point (in hours) when complications began to increase was used to define ‘early’ and ‘delayed’ surgery. To evaluate the robustness of this definition, outcomes amongst propensity-score matched early and delayed patients were compared using percent absolute risk differences (% ARDs, with 95% confidence intervals [CIs]). There were 42,230 patients who met entry criteria. Their mean age was 80.1 (±10.7) and the majority were female (70.5%). The risk of complications modeled by cubic splines consistently increased when wait-times were greater than 24 hours, irrespective of the complication considered. Compared to 13,731 propensity-score matched patients who received surgery earlier, 13,731 patients receiving surgery after 24 hours had a significantly higher risk of 30-day mortality (N=898 versus N=790, % ARD 0.79 [95% CI 0.23 to 1.35], p = .006) and the composite outcome (N=1,680 versus N=1,383, % ARD 2.16 [95% CI 1.43 to 2.89], p < .001). Overall, there were 14,174 patients (33.6%) who received surgery within 24 hours and 28,056 patients (66.4%) who received surgery after 24 hours. Increased wait-time was associated with a greater risk for 30-day mortality and other complications. The finding that a wait-time of 24 hours represents a threshold defining higher risk may inform existing hip fracture guidelines. Since two-thirds of patients did not receive surgery within this timeframe, performance improvement efforts that reduce wait-times are warranted

Introduction. Motion analysis is a validated method of assessing technical dexterity within surgical skills centers. A more accessible and cost-effective method of skills assessment is to use a global rating scale (GRS). We aimed to perform a validation experiment to compare an arthroscopic GRS against motion analysis for monitoring orthopaedic trainees learning simulated arthroscopic meniscal repairs. Methods. An arthroscopic meniscal repair task on a knee simulator was set up in a bioskills laboratory. Nineteen orthopaedic trainees with no experience of meniscal repair were recruited and their performance assessed whilst undertaking a standardized meniscal repair on 12 occasions. An arthroscopic GRS, assessing parameters such as “depth perception,” “bimanual dexterity,” “instrument handling,” and “final product analysis” was used to evaluate technical skill. Performance was assessed blindly by watching video recordings of the arthroscopic tasks. Dexterity analysis was performed using a motion analysis tracking system which measured “time taken,” “total path length of the subject's hands,” and “number of hand movements”. Results. Motion analysis objectively defined the learning curves and demonstrated significant improvement in performance over the 12 tasks (p< 0.0001). The GRS demonstrated the same learning curve with a significant improvement in performance (p< 0.0001). Importantly, for each individual subject, there was significant improvement in performance as assessed by GRS over the 12 tasks (p< 0.0001). There was a moderate correlation (p< 0.0001) between GRS and all the motion analysis parameters (r values: time=−0.58, path length=−0.58, hand movements=−0.51). Conclusion. Established arthroscopic GRSs have not previously been used to monitor learning curves during complex arthroscopic tasks. The results demonstrate that both the GRS and motion analysis are able to detect performance improvement during such tasks. This further validates the arthroscopic GRS for use in monitoring individual trainees and has the advantage over motion analysis of being directly transferrable to the operating room