Search

In the course of conducting traffic stops, officers have discretion to search motorists for drugs, weapons, and other contraband. There is concern that these search decisions are prone to racial bias, but it has proven difficult to rigorously assess claims of discrimination. Here we develop a new statistical method --- the threshold test --- to test for racial discrimination in motor vehicle searches. We use geographic variation in stop outcomes to infer the effective race-specific standards of evidence that officers apply when deciding whom to search, an approach we formalize with a hierarchical Bayesian latent variable model. This technique mitigates the problems of omitted variables and infra-marginality associated with benchmark and outcome tests for discrimination. On a dataset of 4.5 million police stops in North Carolina, we find that the standard for searching black and Hispanic drivers is considerably lower than the standard for searching white and Asian drivers, a pattern that holds consistently across the 100 largest police departments in the state.

Machine learning is used increasingly in clinical care to improve diagnosis, treatment selection, and health system efficiency. Because machine-learning models learn from historically collected data, populations that have experienced human and structural biases in the past—called protected groups—are vulnerable to harm by incorrect predictions or withholding of resources. This article describes how model design, biases in data, and the interactions of model predictions with clinicians and patients may exacerbate health care disparities. Rather than simply guarding against these harms passively, machine-learning systems should be used proactively to advance health equity. For that goal to be achieved, principles of distributive justice must be incorporated into model design, deployment, and evaluation. The article describes several technical implementations of distributive justice—specifically those that ensure equality in patient outcomes, performance, and resource allocation—and guides clinicians as to when they should prioritize each principle. Machine learning is providing increasingly sophisticated decision support and population-level monitoring, and it should encode principles of justice to ensure that models benefit all patients.

It is often asserted that friends and acquaintances have more similar beliefs and attitudes than do strangers; yet empirical studies disagree over exactly how much diversity of opinion exists within local social networks and, relatedly, how much awareness individuals have of their neighbors' views. This article reports results from a network survey, conducted on the Facebook social networking platform, in which participants were asked about their own political attitudes, as well as their beliefs about their friends' attitudes. Although considerable attitude similarity exists among friends, the results show that friends disagree more than they think they do. In particular, friends are typically unaware of their disagreements, even when they say they discuss the topic, suggesting that discussion is not the primary means by which friends infer each other's views on particular issues. Rather, it appears that respondents infer opinions in part by relying on stereotypes of their friends and in part by projecting their own views. The resulting gap between real and perceived agreement may have implications for the dynamics of political polarization and theories of social influence in general.

Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. RDS data are collected through a snowball mechanism, in which current sample members recruit future sample members. In this paper we present RDS as Markov chain Monte Carlo importance sampling, and we examine the effects of community structure and the recruitment procedure on the variance of RDS estimates. Past work has assumed that the variance of RDS estimates is primarily affected by segregation between healthy and infected individuals. We examine an illustrative model to show that this is not necessarily the case, and that bottlenecks anywhere in the networks can substantially affect estimates. We also show that variance is inflated by a common design feature in which the sample members are encouraged to recruit multiple future sample members. The paper concludes with suggestions for implementing and evaluating RDS studies.

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon?s Mechanical Turk.

Recent studies have examined racial disparities in stop-and-frisk, a widely employed but controversial policing tactic. The statistical evidence, however, has been limited and contradictory. We investigate by analyzing three million stops in New York City over five years, focusing on cases where officers suspected the stopped individual of criminal possession of a weapon (CPW). For each CPW stop, we estimate the ex ante probability that the detained suspect has a weapon. We find that in more than 40% of cases, the likelihood of finding a weapon (typically a knife) was less than 1%, raising concerns that the legal requirement of “reasonable suspicion” was often not met. We further find that blacks and Hispanics were disproportionately stopped in these low hit rate contexts, a phenomenon that we trace to two factors: (1) lower thresholds for stopping individuals — regardless of race — in high-crime, predominately minority areas, particularly public housing; and (2) lower thresholds for stopping minorities relative to similarly situated whites. Finally, we demonstrate that by conducting only the 6% of stops that are statistically most likely to result in weapons seizure, one can both recover the majority of weapons and mitigate racial disparities in who is stopped. We show that this statistically informed stopping strategy can be approximated by simple, easily implemented heuristics with little loss in efficiency.

Summary Steroids and calcineurin inhibitors (CNI) have been mainstays of immunosuppression but both have numerous side effects that are associated with substantial morbidity and mortality. This study was carried out to determine if steroids can be eliminated with early discontinuation of cyclosporine A (CsA) and later discontinuation of mycophenolate mofetil (MMF). Ninety-six patients with kidney transplants were entered into four subgroups of two pilot studies. All patients received Thymoglobulin® induction, rapamycin (RAPA), and the immunonutrients arginine and an oil containing ω-3 fatty acids. Mycophenolate mofetil was started in standard doses and discontinued by 2 years. CsA was given in reduced doses for either 4, 6, or 12 months. Follow-up was 12–36 months. Thirteen first rejection episodes occurred during the first year (14%). Combining all patients, 86% were rejection-free at 1 year, 80% at 2 years and 79% at 3 years. No kidney has been lost to acute rejection. Ninety percent of the 84 patients at risk at the end of the study were steroid-free and 87% were off CNI. Fifty-seven percent of 54 patients with a functioning kidney at 3 years were receiving monotherapy with RAPA. We conclude that this therapeutic strategy is worthy of a prospective multi-center clinical trial.

Logarithmic Sobolev inequalities are a well-studied technique for estimating rates of convergence of Markov chains to their stationary distributions. In contrast to continuous state spaces, discrete settings admit several distinct log Sobolev inequalities, one of which is the subject of this paper. Here we derive modified log Sobolev inequalities for some models of random walk, including the random transposition shuffle and the top-random transposition shuffle on S n , and the walk generated by 3-cycles on A n . As an application, we derive concentration inequalities for these models.

Background. Morbid obesity occurs frequently in patients with renal failure and is associated with an increased mortality, particularly from cardiovascular disease, as well as a marked increase in comorbid conditions affecting quality of life. Morbid obesity is also associated with an increased risk of complications and death in transplant patients and is often a cause for denial for access to transplantation. Methods. Thirty morbidly obese patients with chronic renal failure or transplantation underwent gastric bypass (GBP). Nineteen patients had chronic renal failure at the time of GBP, eight had transplantation followed by GBP, and three had GBP and then transplantation. Results. The reduction in excess body mass index (above 25) after GBP at 1, 2, and 3 years was similar to patients without transplantation or chronic renal failure, approximately 70% at 1 year. Comorbid conditions were diminished in each subset of patients, decreasing their risk for potential cardiovascular complications. One patient died 7.9 years after a GBP and 6.1 years after transplantation from cardiovascular disease related to longstanding diabetes that was present before her renal failure. Conclusions. GBP is a safe and effective means for achieving significant long-term weight loss and relief of comorbid conditions in patients with renal failure on dialysis, in preparation for transplantation, or after transplantation.

Respondent Driven Sampling (RDS) is a novel variant of link tracing sampling that has primarily been used to estimate the characteristics of hard-to-reach groups, such as the HIV prevalence of drug users.1 ‘Seeds’ are selected by convenience from a population of interest (target population) and given coupons. Seeds then use these coupons to recruit other people, who themselves become recruiters. Recruits are given compensation, usually money, for taking part in the survey and also an incentive for recruiting others. This process continues in recruitment ‘waves’ until the survey is stopped. Estimation methods are then applied to account for the biased recruitment, for example, the presumed over-recruitment of people with more acquaintances, in an attempt to generate estimates for the underlying population. RDS has quickly become popular and relied on by major public health organisations, including the US Centers for Disease Control and Prevention and Family Health International, chiefly because it is often found to be an efficient method of recruitment in hard-to-reach groups, but also because of the availability of custom written software incorporating inference methods that are designed to generate estimates that are representative of the wider population of interest, despite the biased sampling. As demonstrated by RDS's popularity,1 there was a clear need for new methods of data collection on hard-to-reach groups. However, RDS has not been without its critics. Its reliance on the target population for recruitment introduced ethicalw1 and sampling concerns.w2 If RDS estimates are overly biased or the variance is unacceptably high, then RDS will be little more than another method of convenience sampling. If these errors can be minimised however, then RDS has the potential to become a very useful survey methodology. In this editorial we highlight that ‘RDS’ includes both data collection and statistical inference methods, discuss the limitations …

Two recent disruptions to the online advertising market are the widespread use of ad-blocking software and proposed restrictions on third-party tracking, trends that are driven largely by consumer concerns over privacy. Both primarily impact display advertising (as opposed to search and native social ads), and affect how retailers reach customers and how content producers earn revenue. It is, however, unclear what the consequences of these trends are. We investigate using anonymized web browsing histories of 14 million individuals, focusing on “retail sessions” in which users visit online sites that sell goods and services. We find that only 3% of retail sessions are initiated by display ads, a figure that is robust to permissive attribution rules and consistent across widely varying market segments. We further estimate the full distribution of how retail sessions are initiated, and find that search advertising is three times more important than display advertising to retailers, and search advertising is itself roughly three times less important than organic web search. Moving to content providers, we find that display ads are shown by 12% of websites, accounting for 32% of their page views; this reliance is concentrated in online publishing (e.g., news outlets) where the rate is 91%. While most consumption is either in the long-tail of websites that do not show ads, or sites like Facebook that show native, first-party ads, moderately sized web publishers account for a substantial fraction of consumption, and we argue that they will be most affected by changes in the display advertising market. Finally, we use estimates of ad rates to judge the feasibility of replacing lost ad revenue with a freemium or donation-based model.

Objective To evaluate the influence of intraperitoneal (IP) antibiotic (AB) prophylaxis on the quality of healing and infection rates of exit sites in peritoneal dialysis catheters. Study Design Twenty-one Sprague-Dawley rats were dialyzed 3 times per day for 6 weeks. Dianeal solution containing AB was used for all the rats during the first 5 days. The animals were randomized on the sixth day into three groups: group A (AB-free after randomization), group B (AB for 3 weeks), and group C (AB during 6 weeks). Scores were given to each exit site according to the observation. Mean scores from each group were compared in an attempt to find significant differences between the groups. Dialysate and exit-site drainage samples were taken weekly for microbiology. Results Eight episodes of peritonitis were diagnosed, six in group A and two in group B. The most common bacteria causing peritonitis were gram-negative rods. The mean scores were not significantly different between groups C and B throughout the study, even after the discontinuation of the prophylaxis. Group A, when compared to the other two groups, had significantly higher scores after the second week and throughout the rest of the study. Conclusion Intraperitoneal antibiotic prophylaxis for 3 weeks after catheter implantation is an effective way to prevent early colonization of exit sites, providing a better healing quality and lower incidence of catheter-related infection. Although the extension of the prophylaxis for 6 weeks seems to be beneficial, it was not statistically proven in this study.

ObjectiveTo determine if peritoneal dialysis modality has an impact on protein losses in dialysate.DesignRetrospective, cross-sectional study.Patients190 patients who had selected peritoneal dialysis were classified into one of four transport categories (high, high-average, low-average, or low) based on standard peritoneal equilibration test results. Patients were then assigned to continuous ambulatory peritoneal dialysis (CAPD) or nightly intermittent peritoneal dialysis (NIPD) based on membrane transport characteristics and individual preferences.ResultsPatients with similar membrane transport characteristics had essentially no differences in dialysate protein and albumin losses whether treated with CAPD or NIPD.ConclusionsAlthough high transporters may be better managed with short -dwell therapies such as nocturnal intermittent peritoneal dialysis or daily ambulatory peritoneal dialysis, consistent marked decreases in protein losses cannot be cited as a benefit of NIPD over CAPD.

Online social networking platforms regularly support hundreds of millions of users, who in aggregate generate substantially more data than can be stored on any single physical server. As such, user data are distributed, or sharded, across many machines. A key requirement in this setting is rapid retrieval not only of a given user's information, but also of all data associated with his or her social contacts, suggesting that one should consider the topology of the social network in selecting a sharding policy. In this paper we formalize the problem of efficiently sharding large social network databases, and evaluate several sharding strategies, both analytically and empirically. We find that random sharding---the de facto standard---results in provably poor performance even when frequently accessed nodes are replicated to many shards. By contrast, we demonstrate that one can substantially reduce querying costs by identifying and assigning tightly knit communities to shards. In particular, our theoretical analysis motivates a novel, scalable sharding algorithm that outperforms both random and location-based sharding schemes.

Online publishing, social networks, and web search have dramatically lowered the costs of producing, distributing, and discovering news articles. Some scholars argue that such technological changes increase exposure to diverse perspectives, while others worry that they increase ideological segregation. We address the issue by examining webbrowsing histories for 50,000 US-located users who regularly read online news. We find that social networks and search engines are associated with an increase in the mean ideological distance between individuals. However, somewhat counterintuitively, these same channels also are associated with an increase in an individual's exposure to material from his or her less preferred side of the political spectrum. Finally, the vast majority of online news consumption is accounted for by individuals simply visiting the home pages of their favorite, typically mainstream, news outlets, tempering the consequences -- both positive and negative -- of recent technological changes. We thus uncover evidence for both sides of the debate, while also finding that the magnitude of the effects is relatively modest

Carbamylated hemoglobin: A potential marker for the adequacy of hemodialysis therapy in end-stage renal failure. Urea can dissociate in vivo to form isocyanic acid which can react with hemoglobin to form carbamylated hemoglobin. Previous work has shown that formation of carbamylated hemoglobin depends upon both the severity and the duration of renal failure. To determine whether carbamylated hemoglobin can be used as an assessment of the adequacy of hemodialysis treatment, we prospectively studied 55 stable patients who regularly attended our hospital dialysis program. Carbamylated hemoglobin was greater in those patients with a Kt/V of ≤ 1.1 compared to those with a Kt/V of > 1.1 (120 ± 8 µgVH/gHb versus 99 ± 7, P < 0.01), and there was a negative correlation with Kt/V (r = -0.37, P = 0.007). There were positive correlations between carbamylated hemoglobin and the time-averaged urea concentration (r = 0.4, P = 0.004), and a negative correlation with the urea reduction ratio (r = -0.37, P = 0.01). Carbamylated hemoglobin may therefore be a useful marker of the degree of uremia, just as glycosylated hemoglobin is used in the assessment of patients with diabetes mellitus.

In nine patients on continuous ambulatory peritoneal dialysis (CAPD) who were in stable condition, the authors measured the effects of an extra daily exchange on small solute removals and clearances and the measured/predicted creatinine production (M/P). The goal was to evaluate the sensitivity of M/P to single exchange noncompliance. Daily collections of urine and dialysate were performed on the usual prescribed program and on the next day with an extra exchange of usual exchange volume. The average increase in drain volume of 26% was associated with an average increase in M/P of 6%. The maximum M/P increase was 15%; the percentage increase in M/P correlated inversely with renal creatinine clearance. Daily total urea clearance normalized to total body water by dialysis and renal function increased 12%. Extrapolating these findings suggests that an average patient with a baseline M/P near 1.0 and renal creatinine clearance of 5 ml/min would require more than 50% drain volume noncompliance to yield a M/P of 1.24 on the complaint day. A 50% noncompliance may not yield a M/P greater than 1.0 on the first compliant day if baseline M/P is less than 0.94. The authors suggest monitoring daily urine and dialysis creatinine output because M/P creatinine is relatively insensitive to noncompliance (particularly in patients with residual renal function) and because there are questions about the accuracy of predictions in patients with renal failure using formulae established in different populations. The authors recommend simply monitoring total daily creatinine output and establishing patients as their own controls. The authors provide a table for the interpretation of changes in creatinine output in conjunction with changes in other routinely monitored parameters.

Many factors can affect interpretation of glycohemoglobin (GHB/HbA1c) measurements in patients with chronic renal failure (CRF). Several reports have suggested that erythrocyte survival is substantially lowered in most patients with CRF; this would be expected to lower GHB results (1)(2)(3)(4)(5)(6). Several reports have also suggested that GHB methods, especially those based on charge separation (e.g., ion-exchange HPLC), may have interference by carbamylated hemoglobin that would be expected to falsely increase GHB results (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). Many of these reports evaluated older assay methods; newer ion-exchange methods may show improved separation of the HbA1c fraction from other hemoglobin adducts (15)(17). Because renal failure is common in patients with diabetes and GHB is widely used as an index of mean blood glucose in these patients, we examined GHB results in patients with CRF by several different GHB assay methods. We also investigated the impact of shortened erythrocyte lifespan by comparing the GHB results obtained for nondiabetic patients with and without CRF. Fifty-five patients with CRF (blood urea nitrogen >400 mg/L) were recruited for this study. Twenty-nine were not receiving dialysis and …

Recent work has demonstrated that Web search volume can “predict the present,” meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

Respondent-driven sampling (RDS) is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries and by leading public health organizations, including the Centers for Disease Control and Prevention in the United States. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. Here we investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow. Moreover, because we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly, it is unlikely that RDS performs any better in practice than in our simulations. Notably, the poor performance of RDS is driven not by the bias but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied.

The success of "infinite-inventory" retailers such as Amazon.com and Netflix has been ascribed to a "long tail" phenomenon. To wit, while the majority of their inventory is not in high demand, in aggregate these "worst sellers," unavailable at limited-inventory competitors, generate a significant fraction of total revenue. The long tail phenomenon, however, is in principle consistent with two fundamentally different theories. The first, and more popular hypothesis, is that a majority of consumers consistently follow the crowds and only a minority have any interest in niche content; the second hypothesis is that everyone is a bit eccentric, consuming both popular and specialty products. Based on examining extensive data on user preferences for movies, music, Web search, and Web browsing, we find overwhelming support for the latter theory. However, the observed eccentricity is much less than what is predicted by a fully random model whereby every consumer makes his product choices independently and proportional to product popularity; so consumers do indeed exhibit at least some a priori propensity toward either the popular or the exotic.Our findings thus suggest an additional factor in the success of infinite-inventory retailers, namely, that tail availability may boost head sales by offering consumers the convenience of "one-stop shopping" for both their mainstream and niche interests. This hypothesis is further supported by our theoretical analysis that presents a simple model in which shared inventory stores, such as Amazon Marketplace, gain a clear advantage by satisfying tail demand, helping to explain the emergence and increasing popularity of such retail arrangements. Hence, we believe that the return-on-investment (ROI) of niche products goes beyond direct revenue, extending to second-order gains associated with increased consumer satisfaction and repeat patronage. More generally, our findings call into question the conventional wisdom that specialty products only appeal to a minority of consumers.

The "algorithmic small-world hypothesis" states that not only are pairs of individuals in a large social network connected by short paths, but that ordinary individuals can find these paths. Although theoretically plausible, empirical evidence for the hypothesis is limited, as most chains in "small-world" experiments fail to complete, thereby biasing estimates of "true" chain lengths. Using data from two recent small-world experiments, comprising a total of 162,328 message chains, and directed at one of 30 "targets" spread across 19 countries, we model heterogeneity in chain attrition rates as a function of individual attributes. We then introduce a rigorous way of estimating true chain lengths that is provably unbiased, and can account for empirically-observed variation in attrition rates. Our findings provide mixed support for the algorithmic hypothesis. On the one hand, it appears that roughly half of all chains can be completed in 6-7 steps--thus supporting the "six degrees of separation" assertion--but on the other hand, estimates of the mean are much longer, suggesting that for at least some of the population, the world is not "small" in the algorithmic sense. We conclude that search distances in social networks are fundamentally different from topological distances, for which the mean and median of the shortest path lengths between nodes tend to be similar.

In sponsored search auctions advertisers typically pay a fixed amount per click that their advertisements receive. In particular, the advertiser and the publisher enter into a contract (e.g., the publisher displays the ad; the advertiser pays the publisher 10 cents per click), and each party's subjective value for such a contract depends on their estimated click-through rates (CTR) for the ad. Starting from this motivating example, we define and analyze a class of contract auctions that generalize the classical second price auction. As an application, we introduce impression-plus-click pricing for sponsored search, in which advertisers pay a fixed amount per impression plus an additional amount if their ad is clicked. Of note, when the advertiser's estimated CTR is higher than the publisher's estimated CTR, both parties find negative click payments advantageous, where the advertiser pays the publisher a premium for the impression but the publisher then pays the advertiser per click.

A deck of $n$ cards is shuffled by repeatedly moving the top card to one of the bottom $k_n$ positions uniformly at random. We give upper and lower bounds on the total variation mixing time for this shuffle as $k_n$ ranges from a constant to $n$. We also consider a symmetric variant of this shuffle in which at each step either the top card is randomly inserted into the bottom $k_n$ positions or a random card from the bottom $k_n$ positions is moved to the top. For this reversible shuffle we derive bounds on the $L^2$ mixing time. Finally, we transfer mixing time estimates for the above shuffles to the lazy top to bottom-$k$ walks that move with probability 1/2 at each step., Published at http://dx.doi.org/10.1214/10505160500000062 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

On complete, non-compact manifolds and infinite graphs, Faber-Krahn inequalities have been used to estimate the rate of decay of the heat kernel. We develop this technique in the setting of finite Markov chains, proving upper and lower mixing time bounds via the spectral profile. This approach lets us recover and refine previous conductance-based bounds of mixing time (including the Morris-Peres result), and in general leads to sharper estimates of convergence rates. We apply this method to several models including groups with moderate growth, the fractal-like Viscek graphs, and the torus, to obtain tight bounds on the corresponding mixing times., 26 pages

Background Animal studies have shown that dietary supplementation with arginine and lipids containing the omega-3 and omega-9 fatty acids prolong allograft survival in animals receiving a short course of low-dose cyclosporine. They also reduce cardiovascular complications and infections in humans. Methods Adult renal transplant patients receiving standard immunosuppression were stratified according to gender, diabetic state, donor source (LD or CD), and first versus repeat transplant, and randomized to receive or not receive supplemental arginine and canola oil (containing both omega-3 and omega-9 fatty acids) twice daily. Patients were followed for a minimum of 3 years. Results Seventy-six patients were randomized to the supplement group (S) and 71 patients to the control group (C). Intent-to-treat analysis revealed that S patients had fewer post-30 day first rejection episodes (5.4%) when compared with the C group (23.7%) (P=0.01) and fewer post-30 day episodes of calcineurin inhibitor (CNI) drug toxicity (9.2% vs. 35.3%, P=0.003). S patients developed new onset diabetes mellitus (NODM) less frequently by 3 years (2.3% vs. 14.5%, P=0.04), had fewer cardiac events (5.0% vs. 17.1%, P=0.05), and fewer episodes of sepsis (6.5% vs. 18.7%, P=0.05). Conclusions Dietary supplementation with L-arginine and canola oil is a safe, inexpensive, and unique treatment, which is associated with decreased rejection rates and CNI toxicity after the first month in renal transplant patients. Due to reductions in NODM and cardiac events, long-term benefits for patient survival may be particularly important.

The procedure of laryngoscopy and endotracheal intubation frequently induces a cardiovascular stress response characterized by hypertension, tachycardia, arrhythmias and increased serum concentration of catecholamines. Our study was undertaken to evaluate and compare the influence of lignocaine and diltiazem on the cardiovascular responses to laryngoscopy and intubation. A total of sixty patients of ASA grade I or II patients, between 18 -60 years of age, of either sex, scheduled for non-cardiac surgery, were selected for the study. Baseline parameters i.e. heart rate, systolic blood pressure, diastolic blood pressure and mean arterial pressure were recorded and same parameters were recorded at one minute interval till the completion of study period. The percentage change (increase or decrease) was calculated and was analyzed statistically. There was minimal change in heart rate, systolic blood pressure, diastolic blood pressure and mean arterial pressure after intubation in patients who were given diltiazem as compared to lignocaine and was more stable drug. We concluded that Diltiazem 0.3 mg/kg is able to attenuate the haemodynamic response better as compared to lignocaine.

A Prospective double blind randomized study was conducted on 60 adults patients undergoing laproscpic surgery. Patients of group ? received butorphanol (25μg/kg) and patients of group II received tramadol (2mg/kg) just before induction of anaesthesia. Haemodynamic parameters including heart rate, systolic and diastolic blood pressure were recorded throughout the surgery. On statistical analysis of data, there was statistically significant increase in heart rate, systolic and diastolic blood pressure in tramadol group as compared to butorphanol group just after intubation, which got settled after 10 minutes of intubation. So, we concluded that Butorphanol (25μg/kg) is a good alternative to tramadol (2mg/Kg) as a balanced anaesthetic technique for laproscopic cholecystectomy.

Peritoneal dialysis (PD) is a safe, effective, low-cost intracorporeal renal replacement modality with some inherent advantages. The peritoneal membrane is a biocompatible, internally located dialyzer which limits ingress of bacteria and their products, removes uremic toxins, allows continuous, gentle 24-hour dialysis and ultrafiltration, while avoiding the use of anticoagulants [l, 2].

Respondent-driven sampling is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries, and by leading public health organisations, including the Centers for Disease Control and Prevention in the USA. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. In this talk, I investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged, and that reported RDS CI are misleadingly narrow. Moreover, it is unlikely RDS performs any better in practice than in our simulations as we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly. Notably, the poor performance of RDS is driven not by the bias, but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied. This work is joint with Matthew Salganik.