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Background: Three-dimensional (3D) laparoscopy improves technical efficacy in laboratory environment, but evidence for clinical benefit is lacking. The aim of this study was to determine whether the 3D laparoscopy is beneficial in transabdominal preperitoneal laparoscopic inguinal hernia repair (TAPP).Method: In this prospective, single-blinded, single-center, superior randomized trial, patients scheduled for TAPP were randomly allocated to either 3D or two-dimensional (2D) TAPP laparoscopic approaches. Patients were excluded if secondary operation was planned, the risk of conversion was high, or the surgeon had less than five previous 3D laparoscopic procedures. Patients were operated on by 13 residents and 3 attendings. The primary endpoint was operation time. The study was registered in ClinicalTrials.gov (NCT02367573).Results: Total 278 patients were randomized between 5th February 2015 and 23rd October 2017. Median operation time was shorter in the 3D group (56.0 min vs. 68.0 min, p < 0.001). 10 (8%) patients in 3D group and 6 (5%) patients in 2D group had clinically significant complications (Clavien-Dindo 2 or higher) (p = 0.440). Rate of hernia recurrence was similar between groups at 1-year follow-up. In the subgroup analyses, operation time was shorter in 3D laparoscopy among attendings, residents, female surgeons, surgeons with perfect stereovision, surgeons with > 50 3D laparoscopic procedures, surgeons with any experience in TAPP, patients with body mass indices < 30, and bilateral inguinal hernia repairs.Conclusion: 3D laparoscopy is beneficial and shortens operation time but does not affect safety or long-term outcomes of TAPP. [ABSTRACT FROM AUTHOR]

Approximate Bayesian computation (ABC) and other likelihood-free inference methods have gained popularity in the last decade, as they allow rigorous statistical inference for complex models without analytically tractable likelihood functions. A key component for accurate inference with ABC is the choice of summary statistics, which summarize the information in the data, but at the same time should be low-dimensional for efficiency. Several dimension reduction techniques have been introduced to automatically construct informative and low-dimensional summaries from a possibly large pool of candidate summaries. Projection-based methods, which are based on learning simple functional relationships from the summaries to parameters, are widely used and usually perform well, but might fail when the assumptions behind the transformation are not satisfied. We introduce a localization strategy for any projection-based dimension reduction method, in which the transformation is estimated in the neighborhood of the observed data instead of the whole space. Localization strategies have been suggested before, but the performance of the transformed summaries outside the local neighborhood has not been guaranteed. In our localization approach the transformation is validated and optimized over validation datasets, ensuring reliable performance. We demonstrate the improvement in the estimation accuracy for localized versions of linear regression and partial least squares, for three different models of varying complexity. [ABSTRACT FROM AUTHOR]

Background: While 3D laparoscopy increases surgical performance under laboratory conditions, it is unclear whether it improves outcomes in real clinical scenarios. The aim of this trial was to determine whether the 3D laparoscopy can enhance surgical efficacy in laparoscopic cholecystectomy (LCC). Method: This prospective randomized controlled study was conducted between February 2015 and April 2017 in a day case unit of an academic teaching hospital. Patients scheduled for elective LCC were assessed for eligibility. The exclusion criteria were: (1) planned secondary operation in addition to LCC, (2) predicted to be high-risk for conversion, and (3) surgeons with less than five previous 3D laparoscopic procedures. Patients were operated on by 12 residents and 3 attendings. The primary endpoint was operation time. All surgeons were tested for stereoaquity (Randot® stereotest). The study was registered in ClinicalTrials.gov (NCT02357589). Results: A total of 210 patients were randomized; 105 to 3D laparoscopy and 104 to 2D laparoscopy. Median operation time as similar in the 3D and 2D laparoscopy groups (49 min vs. 48 min, p = 0.703). Operation times were similar in subgroup analyses for surgeon's sex (male vs. female), surgeon's status (resident vs. attending), surgeon's stereovision (stereopsis 10 vs. less than 10), surgeon's experience (performed 200 LCCs or below versus over 200 LCCs), or patient's BMI (≤ 25 vs. 25–30 vs. > 30). No differences in intra- or postoperative complications were noted between the 3D and 2D groups. Conclusion: 3D laparoscopy did not show any advantages over 2D laparoscopy in LCC. [ABSTRACT FROM AUTHOR]

Gaussian distribution has for several decades been ubiquitous in the theory and practice of statistical classification. Despite the early proposals motivating the use of predictive inference to design a classifier, this approach has gained relatively little attention apart from certain specific applications, such as speech recognition where its optimality has been widely acknowledged. Here we examine statistical properties of different inductive classification rules under a generic Gaussian model and demonstrate the optimality of considering simultaneous classification of multiple samples under an attractive loss function. It is shown that the simpler independent classification of samples leads asymptotically to the same optimal rule as the simultaneous classifier when the amount of training data increases, if the dimensionality of the feature space is bounded in an appropriate manner. Numerical investigations suggest that the simultaneous predictive classifier can lead to higher classification accuracy than the independent rule in the low-dimensional case, whereas the simultaneous approach suffers more from noise when the dimensionality increases. [ABSTRACT FROM AUTHOR]

A general inductive Bayesian classification framework is considered using a simultaneous predictive distribution for test items. We introduce a principle of generative supervised and semi-supervised classification based on marginalizing the joint posterior distribution of labels for all test items. The simultaneous and marginalized classifiers arise under different loss functions, while both acknowledge jointly all uncertainty about the labels of test items and the generating probability measures of the classes. We illustrate for data from multiple finite alphabets that such classifiers achieve higher correct classification rates than a standard marginal predictive classifier which labels all test items independently, when training data are sparse. In the supervised case for multiple finite alphabets the simultaneous and the marginal classifiers are proven to become equal under generalized exchangeability when the amount of training data increases. Hence, the marginal classifier can be interpreted as an asymptotic approximation to the simultaneous classifier for finite sets of training data. It is also shown that such convergence is not guaranteed in the semi-supervised setting, where the marginal classifier does not provide a consistent approximation. [ABSTRACT FROM AUTHOR]

Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently. While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available, described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data sets illustrate well the benefits of incorporating spatial information to such an analysis. [ABSTRACT FROM AUTHOR]