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1.  Do First Opinions Affect Second Opinions? 
Journal of General Internal Medicine  2012;27(10):1265-1271.
Background
Second medical opinions have become commonplace and even mandatory in some health-care systems, as variations in diagnosis, treatment or prognosis may emerge among physicians.
Objective
To evaluate whether physicians’ judgment is affected by another medical opinion given to a patient.
Design
Orthopedic surgeons and neurologists filled out questionnaires presenting eight hypothetical clinical scenarios with suggested treatments. One group of physicians (in each specialty) was told what the other physician’s opinion was (study group), and the other group was not told what it was (control group).
Participants
A convenience sample of 332 physicians in Israel: 172 orthopedic surgeons (45.9% of their population) and 160 neurologists (64.0% of their population).
Measurements
Scoring was by choice of less or more interventional treatment in the scenarios. We used χ2 tests and repeated measures ANOVA to compare these scores between the two groups. We also fitted a cumulative ordinal regression to account for the dependence within each physician’s responses.
Results
Orthopedic surgeons in the study group chose a more interventionist treatment when the other physician suggested an intervention than those in the control group [F (1, 170) = 4.6, p = 0.03; OR = 1.437, 95% CI 1.115-1.852]. Evaluating this effect separately in each scenario showed that in four out of the eight scenarios, they chose a more interventional treatment when the other physician suggested an intervention (scenario 1, p = 0.039; scenario 2, p < 0.001; scenario 3, p = 0.033; scenario 6, p < 0.001). These effects were insignificant among the neurologists [F (1,158) = 0.44, p = 0.51; OR = 1.087, 95% CI 0.811-1.458]. In both specialties there were no differences in responses by level of clinical experience [orthopedic surgeons: F (2, 166) = 0.752, p = 0.473; neurologists: F (2,154) = 1.951, p = 0.146].
Conclusions
The exploratory survey showed that in some cases physicians’ judgments may be affected by other physicians’ opinions, but unaffected in other cases. Weighing previous opinions may yield a more informed clinical decision, yet physicians may be unintentionally influenced by previous opinions. Second opinion has the potential to improve the clinical decision-making processes, and mechanisms are needed to reconcile discrepant opinions.
Electronic supplementary material
The online version of this article (doi:10.1007/s11606-012-2056-y) contains supplementary material, which is available to authorized users.
doi:10.1007/s11606-012-2056-y
PMCID: PMC3445697  PMID: 22539066
second-opinion; differential diagnosis; diagnostic reasoning; medical decision-making; health policy; surgery; orthopedics; neurology; surveys; consultation
2.  Novel Fluorescein Angiography-Based Computer-Aided Algorithm for Assessment of Retinal Vessel Permeability 
PLoS ONE  2013;8(4):e61599.
Purpose
To present a novel method for quantitative assessment of retinal vessel permeability using a fluorescein angiography-based computer algorithm.
Methods
Twenty-one subjects (13 with diabetic retinopathy, 8 healthy volunteers) underwent fluorescein angiography (FA). Image pre-processing included removal of non-retinal and noisy images and registration to achieve spatial and temporal pixel-based analysis. Permeability was assessed for each pixel by computing intensity kinetics normalized to arterial values. A linear curve was fitted and the slope value was assigned, color-coded and displayed. The initial FA studies and the computed permeability maps were interpreted in a masked and randomized manner by three experienced ophthalmologists for statistical validation of diagnosis accuracy and efficacy.
Results
Permeability maps were successfully generated for all subjects. For healthy volunteers permeability values showed a normal distribution with a comparable range between subjects. Based on the mean cumulative histogram for the healthy population a threshold (99.5%) for pathological permeability was determined. Clear differences were found between patients and healthy subjects in the number and spatial distribution of pixels with pathological vascular leakage. The computed maps improved the discrimination between patients and healthy subjects, achieved sensitivity and specificity of 0.974 and 0.833 respectively, and significantly improved the consensus among raters for the localization of pathological regions.
Conclusion
The new algorithm allows quantification of retinal vessel permeability and provides objective, more sensitive and accurate evaluation than the present subjective clinical diagnosis. Future studies with a larger patients’ cohort and different retinal pathologies are awaited to further validate this new approach and its role in diagnosis and treatment follow-up. Successful evaluation of vasculature permeability may be used for the early diagnosis of brain microvascular pathology and potentially predict associated neurological sequelae. Finally, the algorithm could be implemented for intraoperative evaluation of micovascular integrity in other organs or during animal experiments.
doi:10.1371/journal.pone.0061599
PMCID: PMC3634003  PMID: 23626701
3.  Novel 1H low field nuclear magnetic resonance applications for the field of biodiesel 
Background
Biodiesel production has increased dramatically over the last decade, raising the need for new rapid and non-destructive analytical tools and technologies. 1H Low Field Nuclear Magnetic Resonance (LF-NMR) applications, which offer great potential to the field of biodiesel, have been developed by the Phyto Lipid Biotechnology Lab research team in the last few years.
Results
Supervised and un-supervised chemometric tools are suggested for screening new alternative biodiesel feedstocks according to oil content and viscosity. The tools allowed assignment into viscosity groups of biodiesel-petrodiesel samples whose viscosity is unknown, and uncovered biodiesel samples that have residues of unreacted acylglycerol and/or methanol, and poorly separated and cleaned glycerol and water. In the case of composite materials, relaxation time distribution, and cross-correlation methods were successfully applied to differentiate components. Continuous distributed methods were also applied to calculate the yield of the transesterification reaction, and thus monitor the progress of the common and in-situ transesterification reactions, offering a tool for optimization of reaction parameters.
Conclusions
Comprehensive applied tools are detailed for the characterization of new alternative biodiesel resources in their whole conformation, monitoring of the biodiesel transesterification reaction, and quality evaluation of the final product, using a non-invasive and non-destructive technology that is new to the biodiesel research area. A new integrated computational-experimental approach for analysis of 1H LF-NMR relaxometry data is also presented, suggesting improved solution stability and peak resolution.
doi:10.1186/1754-6834-6-55
PMCID: PMC3689644  PMID: 23590829
1H low field nuclear magnetic resonance; Biodiesel; Biodiesel physical properties; Chemometrics; Laplace inversion; Transesterification
5.  Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods 
Low-resolution nuclear magnetic resonance (LR-NMR) relaxometry is a powerful tool that can be harnessed for characterizing constituents in complex materials. Conversion of the relaxation signal into a continuous distribution of relaxation components is an ill-posed inverse Laplace transform problem. The most common numerical method implemented today for dealing with this kind of problem is based on L2-norm regularization. However, sparse representation methods via L1 regularization and convex optimization are a relatively new approach for effective analysis and processing of digital images and signals. In this article, a numerical optimization method for analyzing LR-NMR data by including non-negativity constraints and L1 regularization and by applying a convex optimization solver PDCO, a primal-dual interior method for convex objectives, that allows general linear constraints to be treated as linear operators is presented. The integrated approach includes validation of analyses by simulations, testing repeatability of experiments, and validation of the model and its statistical assumptions. The proposed method provides better resolved and more accurate solutions when compared with those suggested by existing tools. © 2013 Wiley Periodicals, Inc. Concepts Magn Reson Part A 42A: 72–88, 2013.
doi:10.1002/cmr.a.21263
PMCID: PMC3698697  PMID: 23847452
low-resolution NMR; sparse reconstruction; L1 regularization; convex optimization

Results 1-5 (5)