Guy J. Maddern

Bio: Guy J. Maddern is an academic researcher from University of Adelaide. The author has contributed to research in topics: Medicine & Hepatectomy. The author has an hindex of 72, co-authored 595 publications receiving 20809 citations. Previous affiliations of Guy J. Maddern include Leicester General Hospital & Royal Australasian College of Surgeons.

A systematic review of skills transfer after surgical simulation training.

Abstract: Objective: To determine whether skills acquired by simulationbased training transfer to the operative setting. Summary Background Data: The fundamental assumption of simulation-based training is that skills acquired in simulated settings are directly transferable to the operating room, yet little evidence has focused on correlating simulated performance with actual surgical performance. Methods: A systematic search strategy was used to retrieve relevant studies. Inclusion of articles was determined using a predetermined protocol, independent assessment by 2 reviewers, and a final consensus decision. Only studies that reported on the use of simulationbased training for surgical skills training, and the transferability of these skills to the operative setting, were included. Results: Ten randomized controlled trials and 1 nonrandomized comparative study were included in this review. In most cases, simulation-based training was in addition to normal training programs. Only 1 study compared simulation-based training with patient-based training. For laparoscopic cholecystectomy and colonoscopy/sigmoidoscopy, participants who received simulation-based training before undergoing patient-based assessment performed better than their counterparts who did not receive previous simulation training, but improvement was not demonstrated for all measured parameters. Conclusions: Skills acquired by simulation-based training seem to be transferable to the operative setting. The studies included in this review were of variable quality and did not use comparable simulation-based training methodologies, which limited the strength of the conclusions. More studies are required to strengthen the evidence base and to provide the evidence needed to determine the extent to which simulation should become a part of surgical training programs. (Ann Surg 2008;248: 166‐179)

Surgical simulation: a systematic review.

Abstract: Objective: To evaluate the effectiveness of surgical simulation compared with other methods of surgical training.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The Clavien-Dindo classification of surgical complications: five-year experience.

Abstract: Background and Aims:The lack of consensus on how to define and grade adverse postoperative events has greatly hampered the evaluation of surgical procedures. A new classification of complications, initiated in 1992, was updated 5 years ago. It is based on the type of therapy needed to correct the co

Modern Applied Statistics With S

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Medscape

Abstract: Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?