METHODS, SYSTEMS, AND DEVICES FOR SURGICAL ACCESS AND PROCEDURES

The embodiments disclosed herein relate to various medical device components, including components that can be incorporated into robotic and/or in vivo medical devices. Certain embodiments include various actuation system embodiments, including fluid actuation systems, drive train actuation systems, and motorless actuation systems. Additional embodiments include a reversibly lockable tube that can provide access for a medical device to a patient\u27s cavity and further provides a reversible rigidity or stability during operation of the device. Further embodiments include various operational components for medical devices, including medical device arm mechanisms that have both axial and rotational movement while maintaining a relatively compact structure. medical device winch components, medical device biopsy/stapler/clamp mechanisms, and medical device adjustable focus mechanisms

Python I, II, and III CMB Anisotropy Measurement Constraints on Open and Flat-Lambda CDM Cosmogonies

We use Python I, II, and III cosmic microwave background anisotropy data to constrain cosmogonies. We account for the Python beamwidth and calibration uncertainties. We consider open and spatially-flat-Lambda cold dark matter cosmogonies, with nonrelativistic-mass density parameter Omega_0 in the range 0.1--1, baryonic-mass density parameter Omega_B in the range (0.005--0.029) h^{-2}, and age of the universe t_0 in the range (10--20) Gyr. Marginalizing over all parameters but Omega_0, the combined Python data favors an open (spatially-flat-Lambda) model with Omega_0 simeq 0.2 (0.1). At the 2 sigma confidence level model normalizations deduced from the combined Python data are mostly consistent with those drawn from the DMR, UCSB South Pole 1994, ARGO, MAX 4 and 5, White Dish, and SuZIE data sets.Comment: 20 pages, 7 figures, accepted by Ap

In vivo laparoscopic robotics

AbstractRobotic laparoscopic surgery is evolving to include in vivo robotic assistants. The impetus for the development of this technology is to provide surgeons with additional viewpoints and unconstrained manipulators that improve safety and reduce patient trauma. A family of these robots have been developed to provide vision and task assistance. Fixed-base and mobile robots have been designed and tested in animal models with much success. A cholecystectomy, prostatectomy, and nephrectomy have all been performed with the assistance of these robots. These early successful tests show how in vivo laparoscopic robotics may be part of the next advancement in surgical technology

In vivo laparoscopic robotics

Robotic laparoscopic surgery is evolving to include in vivo robotic assistants. The impetus for the development of this technology is to provide surgeons with additional viewpoints and unconstrained manipulators that improve safety and reduce patient trauma. A family of these robots have been developed to provide vision and task assistance. Fixed-base and mobile robots have been designed and tested in animal models with much success. A cholecystectomy, prostatectomy, and nephrectomy have all been performed with the assistance of these robots. These early successful tests show how in vivo laparoscopic robotics may be part of the next advancement in surgical technology

Time-modified Confounding

According to the authors, time-modified confounding occurs when the causal relation between a time-fixed or time-varying confounder and the treatment or outcome changes over time. A key difference between previously described time-varying confounding and the proposed time-modified confounding is that, in the former, the values of the confounding variable change over time while, in the latter, the effects of the confounder change over time. Using marginal structural models, the authors propose an approach to account for time-modified confounding when the relation between the confounder and treatment is modified over time. An illustrative example and simulation show that, when time-modified confounding is present, a marginal structural model with inverse probability-of-treatment weights specified to account for time-modified confounding remains approximately unbiased with appropriate confidence limit coverage, while models that do not account for time-modified confounding are biased. Correct specification of the treatment model, including accounting for potential variation over time in confounding, is an important assumption of marginal structural models. When the effect of confounders on either the treatment or outcome changes over time, time-modified confounding should be considered

Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies

Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size

A simulation study of finite-sample properties of marginal structural Cox proportional hazards models

Motivated by a previously published study of HIV treatment, we simulated data subject to time-varying confounding affected by prior treatment to examine some finite-sample properties of marginal structural Cox proportional hazards models. We compared (a) unadjusted, (b) regression-adjusted, (c) unstabilized and (d) stabilized marginal structural (inverse probability-of-treatment [IPT] weighted) model estimators of effect in terms of bias, standard error, root mean squared error (MSE) and 95% confidence limit coverage over a range of research scenarios, including relatively small sample sizes and ten study assessments. In the base-case scenario resembling the motivating example, where the true hazard ratio was 0.5, both IPT-weighted analyses were unbiased while crude and adjusted analyses showed substantial bias towards and across the null. Stabilized IPT-weighted analyses remained unbiased across a range of scenarios, including relatively small sample size; however, the standard error was generally smaller in crude and adjusted models. In many cases, unstabilized weighted analysis showed a substantial increase in standard error compared to other approaches. Root MSE was smallest in the IPT-weighted analyses for the base-case scenario. In situations where time-varying confounding affected by prior treatment was absent, IPT-weighted analyses were less precise and therefore had greater root MSE compared with adjusted analyses. The 95% confidence limit coverage was close to nominal for all stabilized IPT-weighted but poor in crude, adjusted, and unstabilized IPT-weighted analysis. Under realistic scenarios, marginal structural Cox proportional hazards models performed according to expectations based on large-sample theory and provided accurate estimates of the hazard ratio

An information criterion for marginal structural models

Marginal structural models were developed as a semiparametric alternative to the G-computation formula to estimate causal effects of exposures. In practice, these models are often specified using parametric regression models. As such, the usual conventions regarding regression model specification apply. This paper outlines strategies for marginal structural model specification, and considerations for the functional form of the exposure metric in the final structural model. We propose a quasi-likelihood information criterion adapted from use in generalized estimating equations. We evaluate the properties of our proposed information criterion using a limited simulation study. We illustrate our approach using two empirical examples. In the first example, we use data from a randomized breastfeeding promotion trial to estimate the effect of breastfeeding duration on infant weight at one year. In the second example, we use data from two prospective cohorts studies to estimate the effect of highly active antiretroviral therapy on CD4 count in an observational cohort of HIV-infected men and women. The marginal structural model specified should reflect the scientific question being addressed, but can also assist in exploration of other plausible and closely related questions. In marginal structural models, as in any regression setting, correct inference depends on correct model specification. Our proposed information criterion provides a formal method for comparing model fit for different specifications

The Impact of Tobacco Control Policies on Smoking Among Socioeconomic Groups in Nine European Countries, 1990-2007

Background: It is uncertain whether tobacco control policies have contributed to a narrowing or widening of socioeconomic inequalities in smoking in European countries during the past two decades. This paper aims to investigate the impact of price and non-price related population-wide tobacco control policies on smoking by socioeconomic group in nine European countries between 1990 and 2007. Methods: Individual-level education, occupation and smoking status were obtained from nationally representative surveys. Country-level price-related tobacco control policies were measured by the relative price of cheapest cigarettes and of cigarettes in the most popular price category. Country-level non-price policies were measured by a summary score covering four policy domains: smoking bans or restrictions in public places and workplaces, bans on advertising and promotion, health warning labels, and cessation services. The associations between policies and smoking were explored using logistic regressions, stratified by education and occupation, and adjusted for age, Gross Domestic Product, period and country fixed effects. Results: The price of popular cigarettes and non-price policies were negatively associated with smoking among men. The price of the cheapest cigarettes was negatively associated with smoking among women. While these favorable effects were generally in the same direction for all socioeconomic groups, they were larger and statistically significant in lower socioeconomic groups only. Conclusions: Tobacco control policies as implemented in nine European countries, have probably helped to reduce the prevalence of smoking in the total population, particularly in lower socioeconomic groups. Widening inequalities in smoking may be explained by other factors. Policies with larger effects on lower socioeconomic groups are needed to reverse this trend. Implications: Socioeconomic inequalities in smoking widened between the 1990s and the 2000s in Europe. During the same period, there were intensified tobacco control policies in many European countries. It is uncertain whether tobacco control policies have contributed to a narrowing or widening of socioeconomic inequalities in smoking in European countries. This study shows that tobacco control policies as implemented in the available European countries have helped to reduce the prevalence of smoking in the total population, particularly in lower socioeconomic groups. Widening inequalities in smoking may be explained by other factors.Peer reviewe