Surgical Theatre, Gifted Performances: The Changing Moral Economy of Surgical Training

Fred Hollows and his work to reduce blindness in Indigenous communities is an obvious example of benevolence of doctors and nurses towards patients while the role of the staff of burns units around Australia in treating the victims of the Bali bombing is another. Some different stories about benevolence in medicine, concerning the benevolence of patients towards trainee clinical staff are suggested.

Pig eye trabeculectomy - a wet-lab teaching model

Purpose A teaching model for trabeculectomy is described using pig eyes prepared in formalin. Method The model enables trainee surgeons to practice various aspects of tissue handling required for successful trabeculectomy including the construction of a fornix-based conjunctival flap, scleral flap with buried releasable sutures, and water-tight conjunctival closure. Results Exposure to the necessary skills required to perform trabeculectomy surgery can be improved by the use of wet laboratory practice. Conclusions Trabeculectomy surgery experience is becoming more limited as fewer procedures are being performed due to the efficacy of recent medications. Wet laboratories will become an increasingly important aspect of a comprehensive ophthalmology training programme.

Why people underestimate y when extrapolating in linear functions

E. L. DeLosh, J. R. Busemeyer, and M. A. McDaniel (1997) found that when learning a positive, linear relationship between a continuous predictor (x) and a continuous criterion (y), trainees tend to underestimate y on items that ask the trainee to extrapolate. In 3 experiments, the authors examined the phenomenon and found that the tendency to underestimate y is reliable only in the so-called lower extrapolation region-that is, new values of x that lie between zero and the edge of the training region. Existing models of function learning, such as the extrapolation-association model (DeLosh et al., 1997) and the population of linear experts model (M. L. Kalish, S. Lewandowsky, & J. Kruschke, 2004), cannot account for these results. The authors show that with minor changes, both models can predict the correct pattern of results.