When I was a dreamer: and you were my dream

https://digitalcommons.ithaca.edu/sheetmusic/1213/thumbnail.jp

Teratogenic and Embryocidal Effects of Zidovudine (AZT) in Sprague-Dawley Rats

Objective: The purpose of the present investigation was to analyze the effets of zidovudine on the postimplantation embryo and fetus

Rater Reliability of the Hardy Classification for Pituitary Adenomas in the Magnetic Resonance Imaging Era

Objectives The Hardy classification is used to classify pituitary tumors for clinical and research purposes. The scale was developed using lateral skull radiographs and encephalograms, and its reliability has not been evaluated in the magnetic resonance imaging (MRI) era. Design Fifty preoperative MRI scans of biopsy-proven pituitary adenomas using the sellar invasion and suprasellar extension components of the Hardy scale were reviewed. Setting This study was a cohort study set at a single institution. Participants There were six independent raters. Main Outcome Measures The main outcome measures of this study were interrater reliability, intrarater reliability, and percent agreement. Results Overall interrater reliability of both Hardy subscales on MRI was strong. However, reliability of the intermediate scores was weak, and percent agreement among raters was poor (12-16%) using the full scales. Dichotomizing the scale into clinically useful groups maintained strong interrater reliability for the sellar invasion scale and increased the percent agreement for both scales. Conclusion This study raises important questions about the reliability of the original Hardy classification. Editing the measure to a clinically relevant dichotomous scale simplifies the rating process and may be useful for preoperative tumor characterization in the MRI era. Future research studies should use the dichotomized Hardy scale (sellar invasion Grades 0-III versus Grade IV, suprasellar extension Types 0-C versus Type D)

New developments in canine hepatozoonosis in North America: a review

Canine hepatozoonosis is caused by Hepatozoon canis and Hepatozoon americanum, apicomplexan parasites transmitted to dogs by ingestion of infectious stages. Although the two agents are phylogenetically related, specific aspects, including characteristics of clinical disease and the natural history of the parasites themselves, differ between the two species. Until recently, H. canis infections had not been clearly documented in North America, and autochthonous infection with H. americanum has yet to be reported outside of the southern United States. However, recent reports demonstrate H. canis is present in areas of North America where its vector tick, Rhipicephalus sanguineus, has long been endemic, and that the range of H. americanum is likely expanding along with that of its vector tick, Amblyomma maculatum; co-infections with the two organisms have also been identified. Significant intraspecific variation has been reported in the 18S rRNA gene sequence of both Hepatozoon spp.-infecting dogs, suggesting that each species may represent a complex of related genogroups rather than well-defined species. Transmission of H. americanum to dogs via ingestion of cystozoites in muscle of infected vertebrates was recently demonstrated, supporting the concept of predation as a means of natural transmission. Although several exciting advances have occurred in recent years, much remains to be learned about patterns of infection and the nature of clinical disease caused by the agents of canine hepatozoonosis in North America

Does self-monitoring reduce blood pressure? Meta-analysis with meta-regression of randomized controlled trials

Introduction. Self-monitoring of blood pressure (BP) is an increasingly common part of hypertension management. The objectives of this systematic review were to evaluate the systolic and diastolic BP reduction, and achievement of target BP, associated with self-monitoring. Methods. MEDLINE, Embase, Cochrane database of systematic reviews, database of abstracts of clinical effectiveness, the health technology assessment database, the NHS economic evaluation database, and the TRIP database were searched for studies where the intervention included self-monitoring of BP and the outcome was change in office/ambulatory BP or proportion with controlled BP. Two reviewers independently extracted data. Meta-analysis using a random effects model was combined with meta-regression to investigate heterogeneity in effect sizes. Results. A total of 25 eligible randomized controlled trials (RCTs) (27 comparisons) were identified. Office systolic BP (20 RCTs, 21 comparisons, 5,898 patients) and diastolic BP (23 RCTs, 25 comparisons, 6,038 patients) were significantly reduced in those who self-monitored compared to usual care (weighted mean difference (WMD) systolic −3.82 mmHg (95% confidence interval −5.61 to −2.03), diastolic −1.45 mmHg (−1.95 to −0.94)). Self-monitoring increased the chance of meeting office BP targets (12 RCTs, 13 comparisons, 2,260 patients, relative risk = 1.09 (1.02 to 1.16)). There was significant heterogeneity between studies for all three comparisons, which could be partially accounted for by the use of additional co-interventions. Conclusion. Self-monitoring reduces blood pressure by a small but significant amount. Meta-regression could only account for part of the observed heterogeneity

Evaluating Student Volunteer and Service-Learning Programs: A Casebook for Practitioners

Today, evaluation concepts and methods are widely available to those who plan and administer student volunteer programs. Unfortunately, however, evaluation has all too often been carried out-and written about-in ways that have robbed it of its usefulness to people dealing with the realities of day-to-day program operation. Evaluation has thus acquired the reputation among practitioners of being too complex, too costly, too time-consuming, even too threatening to be of much practical value

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Statistical methods to correct for verification bias in diagnostic studies are inadequate when there are few false negatives: a simulation study

<p>Abstract</p> <p>Background</p> <p>A common feature of diagnostic research is that results for a diagnostic gold standard are available primarily for patients who are positive for the test under investigation. Data from such studies are subject to what has been termed "verification bias". We evaluated statistical methods for verification bias correction when there are few false negatives.</p> <p>Methods</p> <p>A simulation study was conducted of a screening study subject to verification bias. We compared estimates of the area-under-the-curve (AUC) corrected for verification bias varying both the rate and mechanism of verification.</p> <p>Results</p> <p>In a single simulated data set, varying false negatives from 0 to 4 led to verification bias corrected AUCs ranging from 0.550 to 0.852. Excess variation associated with low numbers of false negatives was confirmed in simulation studies and by analyses of published studies that incorporated verification bias correction. The 2.5^th– 97.5^thcentile range constituted as much as 60% of the possible range of AUCs for some simulations.</p> <p>Conclusion</p> <p>Screening programs are designed such that there are few false negatives. Standard statistical methods for verification bias correction are inadequate in this circumstance.</p

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

<p>Abstract</p> <p>Background</p> <p>The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.</p> <p>Methods</p> <p>Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained.</p> <p>Results</p> <p>CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness.</p> <p>Conclusions</p> <p>Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.</p

Quantitative Factors Proposed to Influence the Prevalence of Canine Tick-Borne Disease Agents in the United States

The Companion Animal Parasite Council hosted a meeting to identify quantifiable factors that can influence the prevalence of tick-borne disease agents among dogs in North America. This report summarizes the approach used and the factors identified for further analysis with mathematical models of canine exposure to tick-borne pathogens