Neural networks were trained to classify single 20 ms frames of vowels using either perceptually-based spectral representations or LPC spectra as input. Classification performance was compared with performance of several distance measures using nearest-neighbor and mean-distance decision criteria. T
Review of methods for measuring and comparing center performance after organ transplantation
β Scribed by James Neuberger; Sue Madden; David Collett
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 309 KB
- Volume
- 16
- Category
- Article
- ISSN
- 1527-6465
- DOI
- 10.1002/lt.22131
No coin nor oath required. For personal study only.
β¦ Synopsis
The assessment of outcomes after transplantation is important for several reasons: it provides patients with data so that they can make informed decisions about the benefits of transplantation and the success of the transplant unit; it informs commissioners that resources are allocated properly; and it provides clinicians reassurance that results are acceptable or, if they are not, provides early warning so that problems can be identified, corrections can be instituted early, and all interested parties can be reassured that scarce resources are used fairly. The need for greater transparency in reporting outcomes after liver transplantation and for comparisons both between and within centers has led to a number of approaches being adopted for monitoring center performance. We review some of the commonly used methods, highlight their strengths and weaknesses, and concentrate on methods that incorporate risk adjustment. Measuring and comparing outcomes after transplantation is complex, and there is no single approach that gives a complete picture. All those using analyses of outcomes must understand the merits and limitations of individual methods. When used properly, such methods are invaluable in ensuring that a scarce resource is used effectively, any adverse trend in outcomes is identified promptly and remedied, and best performers are identified; they thus allow the sharing of best practices. However, when they are used inappropriately, such measurements may lead to inappropriate conclusions, encourage risk-averse behavior, and discourage innovation.
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