Mechanical Work as an Indirect Measure of Subjective Costs Influencing Human Movement

To descend a flight of stairs, would you rather walk or fall? Falling seems to have some obvious disadvantages such as the risk of pain or injury. But the preferred strategy of walking also entails a cost for the use of active muscles to perform negative work. The amount and distribution of work a person chooses to perform may, therefore, reflect a subjective valuation of the trade-offs between active muscle effort and other costs, such as pain. Here we use a simple jump landing experiment to quantify the work humans prefer to perform to dissipate the energy of landing. We found that healthy normal subjects (N = 8) preferred a strategy that involved performing 37% more negative work than minimally necessary (P<0.001) across a range of landing heights. This then required additional positive work to return to standing rest posture, highlighting the cost of this preference. Subjects were also able to modulate the amount of landing work, and its distribution between active and passive tissues. When instructed to land softly, they performed 76% more work than necessary (P<0.001), with a higher proportion from active muscles (89% vs. 84%, P<0.001). Stiff-legged landings, performed by one subject for demonstration, exhibited close to the minimum of work, with more of it performed passively through soft tissue deformations (at least 30% in stiff landings vs. 16% preferred). During jump landings, humans appear not to minimize muscle work, but instead choose to perform a consistent amount of extra work, presumably to avoid other subjective costs. The degree to which work is not minimized may indirectly quantify the relative valuation of costs that are otherwise difficult to measure

Robot-assisted liver surgery in a general surgery unit with a "referral centre hub&spoke learning program". Early outcomes after our first 70 consecutive patients

BACKGROUND: The aim of this study was to evaluate safety, feasibility and short-term outcomes of our first 70 consecutive patients treated by robotic-assisted liver resection after a reversal proctoring between a high HPB volume centre and our well-trained center in minimally invasive General Surgery. Six surgeons were involved in this Hub&Spoke learning program. METHODS: From September 2012 to December 2016, 70 patients underwent robotic-assisted liver resections (RALR). We treated 18 patients affected by colorectal and gastric cancer with synchronous liver lesions suspected for metastases in a one-stage robotic-assisted procedure. For the first 20 procedures we had a tutor in the operatory room, who was present also in the next most difficult procedures. RESULTS: The 30-and 90-day mortality rate was zero with an overall morbidity rate of 10.1%. Associated surgical procedures were performed in about 65,7% of patients. The observed conversion rate was 10%. The results of the first 20 cases were similar to the next 50 showing a shortned learning curve. CONCLUSIONS: Minimally invasive robot-assisted liver resection is a safe technique; it allows overcoming many limits of conventional laparoscopy. This innovative, time-enduring Hub&Spoke may allow patients to undergo a proper standard of care also for complex surgical procedures, without the need of reaching referral centres

Alternative Specifications of Reference Income Levels in the Income Stabilization Tool

In this chapter, different approaches for the specification of reference income levels in the income stabilization tool (IST) are analyzed. The current proposal of the European Commission suggests a 3-year average or a 5-year Olympic average to specify the farm-level reference income that is used to identify if and to what extent a farmer is indemnified in a specific year. Using Monte Carlo simulations, we investigate the impact of income trends on indemnification if these average-based methods are used in the IST. In addition, we propose and investigate a regression-based approach that considers observed income trends to specify reference income levels. Furthermore, we apply these three different approaches to farm-level panel data from Swiss agriculture for the period 2003–2009. We find that average-based approaches cause lower than expected indemnification levels for farmers with increasing incomes, and higher indemnifications if farm incomes are decreasing over time. Small income trends are sufficient to cause substantial biases between expected (fair) and realized indemnification payments at the farm level. In the presence of income trends, average-based specifications of reference income levels will thus cause two major problems for the IST. First, differences between expected and realized indemnification levels can lead to significant mismatches between expected and real costs of the IST. Second, indemnity levels that do not reflect farm-level income losses do not allow achieving the actual purpose of the IST of securing farm incomes. Our analysis shows that a regression-based approach to specify reference income levels can contribute to bound potential biases in cases of decreasing or increasing income levels