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1.
As medical programs place increasing importance on competency-based training and surgical simulations for residents, anatomy laboratories, and body donation programs find themselves in a position of adapting to changing demands. To better assess the demand for “life-like” cadaveric specimens and evaluate the possible impacts that competency-based medical education could have upon the body donation program of McGill University, Canada, the authors tracked, over the course of the last 10 years, the number of soft-embalmed specimens, along with the number of teaching sessions and the residents enrolled in competency-based programs that are using cadaveric material. The results reveal that the number of soft-embalmed specimens used within residency training increased from 5 in 2009 to 35 in 2019, representing an increase from 6% of bodies to 36.5% of the total number of body donors embalmed in this institution. Correspondingly, the number of annual teaching sessions for residents increased from 19 in 2012 to 116 in 2019. These increases in teaching are correlated with increasing number of residents enrolled in competency-based programs over the last 3 years (Pearson r ranging from 0.9705 to 0.9903, and R2 ranging from 0.9418 to 0.9808). Those results suggest that the new skill-centered curricula which require residents to perform specific tasks within realistic settings, exhibit a growing demand for “life-like” cadaveric specimens. Institutions’ body donation programs must, therefore, adapt to those greater need for cadaveric specimens, which presents many challenges, ranging from the logistical to the ethical. 相似文献
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Searching online information resources using mobile devices is affected by small screens which can display only a fraction
of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user
feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach
we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing
all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance
feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ)
and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking
that attempts to maximize information gain from the user’s choices and the top-D ranked documents. Experimental results indicate
that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For
the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a
user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings
from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm,
coupled with the sampled display, is the most effective.
Extended version of “Evaluating Relevance Feedback Algorithms for Searching on Small Displays, ” Vishwa Vinay, Ingemar J.
Cox, Natasa Milic-Frayling, Ken Wood published in the proceedings of ECIR 2005, David E. Losada, Juan M. Fernández-Luna (Eds.),
Springer 2005, ISBN 3-540-25295-9 相似文献
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