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This chapter explores four approaches to personal identity in terms of numerical identity: person-based accounts, a biological account, a mind-based account, and a social account. We also introduce the concept of narrative identity, which involves a person’s self-conception or self-told story about their life. We argue, first, that person-based accounts and the social account are implausible accounts of numerical identity and, second, that both the mind-based and biological accounts are plausible. This motivates a pluralistic approach to personal identity in the sense that policies and practices should be consistent with both accounts. With these theoretical resources, we turn to three practical issues. First, we neutralize some concerns about human enhancement through biomedical means. Second, we investigate and ultimately vindicate the authority of advance directives in cases of severe dementia. Finally, we address the definition of death and associated questions about unilateral discontinuation of life support and vital organ procurement. Proper resolution of these issues turns primarily on practical considerations other than the nature of death.
Simulation training has become a core component in the training of ENT surgeons. It provides the opportunity for the repetitive practice of a surgical technique. Simulators are broadly categorised into low- and high-fidelity simulators. A method using a home microprocessor to enhance a low-fidelity surgical simulator is introduced.
Method
The Yorick tonsil tie trainer was enhanced using an Arduino microcontroller attached to the simulated inferior pole of the tonsil. The Arduino was coded to give a visual stimulus when linear motion exceeded parameters. The prototype simulator was tested to gain information on whether the enhancement could identify differences between novice and expert users.
Conclusion
An enhanced low-fidelity tonsil trainer was produced using a low-cost, simple home microprocessing board. The enhanced simulator gives objective feedback allowing for self-directed learning. Further research is required to evaluate the benefits of these enhancements above non-enhanced simulation training.
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