Everything is Bigger in Texas, Including the Latino Adolescent Pregnancy Rate: How Do We Eliminate the Epidemic of Latino Teen Pregnancy?

A commentary on Tortolero et al.'s article entitled, "Latino Teen Pregnancy in Texas: Prevalence, Prevention, and Policy."

In Case of Emergency: Implementing Disaster Clauses in Publisher Contracts

Joanne Romano, Licensing and Serials Librarian for The Texas Medical Center Library, presented “In Case of Emergency--Implementing Disaster Clauses in Publisher Contracts” to the National Network of Libraries of Medicine/Southeastern/Atlantic Region’s Emergency Response and Preparedness Advisory Committee, (NN/LM-SE/A ERAC) on November 17, 2010, in St. Petersburg, FLA at the Marriott Vinoy Renaissance Resort. Included were slides of the devastation after the 8.8 magnitude earthquake in the Maule region of Chile, how The TMC Library assisted, lessons learned, and advice for how to include disaster clauses in publisher licenses. The NN/LM-SE/A ERAC group invited Ms. Romano to present at their bi-meeting after learning of her library’s key role from other NLM officers. As a result, Ms. Romano was then invited as a guest speaker on for NN/LM-SE/A region’s annual webinar, “Beyond the Sea”, which also included speakers from John Wiley & Sons, Inc., the publisher who worked with The TMC Library in providing emergency access to researchers at the University de Talca, Talca, Chile.

Connecting the Dots: Families and Children with Special Needs in a Rural Community

Due to the rising number of children with disabilities, the needs of these families must be addressed. This article describes the development and implementation of a regional forum in a rural community to address education and training needs of families and professionals. The Special Needs Summit provided workshops, information, and activities for parents and professionals. Participants were invited to participate in a study through a survey soliciting feedback regarding the importance and effectiveness of the training and information received through the Summit, gaps in resources, and future educational and training needs. Overall, participants gave satisfactory ratings regarding the training and education provided during the forum, and gave direction for future programming.

Fluorophotometry is a sensitive method/technique to evaluate epithelial permeability in murine experimental dry eye

Purpose. Fluorophotometry is a well validated method for assessing corneal permeability in human subjects. However, with the growing importance of basic science animal research in ophthalmology, fluorophotometry’s use in animals must be further evaluated. The purpose of this study was to evaluate corneal epithelial permeability following desiccating stress using the modified Fluorotron Master™. ^ Methods. Corneal permeability was evaluated prior to and after subjecting 6-8 week old C57BL/6 mice to experimental dry eye (EDE) for 2 and 5 days (n=9/time point). Untreated mice served as controls. Ten microliters of 0.001% sodium fluorescein (NaF) were instilled topically into each mouse’s left eye to create an eye bath, and left to permeate for 3 minutes. The eye bath was followed by a generous wash with Buffered Saline Solution (BSS) and alignment with the Fluorotron Master™. Seven corneal scans using the Fluorotron Master were performed during 15 minutes (1 st post-wash scans), followed by a second wash using BSS and another set of five corneal scans (2nd post-wash scans) during the next 15 minutes. Corneal permeability was calculated using data calculated with the FM™ Mouse software. ^ Results. When comparing the difference between the Post wash #1 scans within the group and the Post wash #2 scans within the group using a repeated measurement design, there was a statistical difference in the corneal fluorescein permeability of the Post-wash #1 scans after 5 days (1160.21±108.26 vs. 1000.47±75.56 ng/mL, P<0.016 for UT-5 day comparison 8 [0.008]), but not after only 2 days of EDE compared to Untreated mice (1115.64±118.94 vs. 1000.47±75.56 ng/mL, P>0.016 for UT-2 day comparison [0.050]). There was no statistical difference between the 2 day and 5 day Post wash #1 scans (P=.299). The Post-wash #2 scans demonstrated that EDE caused a significant NaF retention at both 2 and 5 days of EDE compared to baseline, untreated controls (1017.92±116.25, 1015.40±120.68 vs. 528.22±127.85 ng/mL, P<0.05 [0.0001 for both]). There was no statistical difference between the 2 day and 5 day Post wash #2 scans (P=.503). The comparison between the Untreated post wash #1 with untreated post wash #2 scans using a Paired T-test showed a significant difference between the two sets of scans (P=0.000). There is also a significant difference between the 2 day comparison and the 5 day comparison (P values = 0.010 and 0.002, respectively). ^ Conclusion. Desiccating stress increases permeability of the corneal epithelium to NaF, and increases NaF retention in the corneal stroma. The Fluorotron Master is a useful and sensitive tool to evaluate corneal permeability in murine dry eye, and will be a useful tool to evaluate the effectiveness of dry eye treatments in animal-model drug trials.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

The Role of the Clinical Educator in Evaluating Nursing Competency

INTRODUCTION: Evaluation of nursing competency is critical to assuring patient safety and maintaining high professional standards in the practice of nursing. All nurses must graduate from an approved nursing program and successfully pass the national board exam before receiving initial licensure. State boards of nursing fulfill the role of gatekeeper, seeking to assure the public that nurses provide safe, competent care. In turn, high public awareness and patient advocacy initiatives require close monitoring of nursing competency. [See PDF for complete abstract]