Preference Elicitation Tool for Abnormal Uterine Bleeding Treatment: A Randomized Controlled Trial

Background It is estimated that one-third of women will experience abnormal menstrual bleeding. The majority of these cases are not due to cancer or pregnancy complications and, as a result, women are faced with a variety of treatment alternatives, the selection of which is largely dependent on personal preferences for care rather than clinical outcomes. Objective This randomized trial was designed to evaluate a preference elicitation tool to promote physician–patient collaborative decision making for treatment of abnormal uterine bleeding (AUB). Methods Adaptive conjoint analysis (ACA) was used to create a preference elicitation tool in English and in Spanish. Women with AUB were enrolled to the study and randomly assigned to ACA or usual counseling at the initial clinic visit at four clinics (three in Indianapolis, IN, USA, and one in Southern Pines, NC, USA). The ACA tool elicited preferences across eight attributes: treatment efficacy; sexual function; medical care; cost; fertility; frequency of medication use; permanence; and recovery time. t tests were used to compare differences in the primary outcomes of decision regret and treatment satisfaction at the follow-up visit. The study was designed to have 80 % power to detect significant differences between groups for the primary outcomes of regret and satisfaction. Results Women were enrolled in the study between September 2009 and March 2012. 183 participants were randomized to ACA and 191 to usual counseling. Overall, mean (standard deviation) treatment satisfaction was high at 35.71 (9.72) (scale of 0–44), and decision regret was low at 25.9 (21.0) (scale of 0–100), creating ceiling effects for the selected outcome variables; there were no significant differences between the ACA and control groups at the follow-up assessment. There was a strong inverse relationship between age and decision regret (p = 0.007). Exploratory subgroup analysis in the youngest quartile comprising 64 women aged 19–35 years showed a statistically non-significant difference in mean regret scores for the ACA group versus usual counseling (24.6 vs. 34.6, respectively; p = 0.08). Conclusions A preference elicitation tool at the initial consultation visit did not reduce decision regret or improve treatment satisfaction among patients with AUB; however, there is a need for additional research to further understand this tool’s potential role in promoting collaborative decision making, which may be particularly important among younger women

The GEOTRACES Intermediate Data Product 2014

The GEOTRACES Intermediate Data Product 2014 (IDP2014) is the first publicly available data product of the international GEOTRACES programme, and contains data measured and quality controlled before the end of 2013. It consists of two parts: (1) a compilation of digital data for more than 200 trace elements and isotopes (TEIs) as well as classical hydrographic parameters, and (2) the eGEOTRACES Electronic Atlas providing a strongly inter-linked on-line atlas including more than 300 section plots and 90 animated 3D scenes. The IDP2014 covers the Atlantic, Arctic, and Indian oceans, exhibiting highest data density in the Atlantic. The TEI data in the IDP2014 are quality controlled by careful assessment of intercalibration results and multi-laboratory data comparisons at cross-over stations. The digital data are provided in several formats, including ASCII spreadsheet, Excel spreadsheet, netCDF, and Ocean Data View collection. In addition to the actual data values the IDP2014 also contains data quality flags and 1-? data error values where available. Quality flags and error values are useful for data filtering. Metadata about data originators, analytical methods and original publications related to the data are linked to the data in an easily accessible way. The eGEOTRACES Electronic Atlas is the visual representation of the IDP2014 data providing section plots and a new kind of animated 3D scenes. The basin-wide 3D scenes allow for viewing of data from many cruises at the same time, thereby providing quick overviews of large-scale tracer distributions. In addition, the 3D scenes provide geographical and bathymetric context that is crucial for the interpretation and assessment of observed tracer plumes, as well as for making inferences about controlling processes

Veteran Certification: Demystify and Align

According to IVMF’s National Survey of Military Affiliated Entrepreneurs, 55% of veterans reported business certification as a barrier because of its difficult process. Veteran Certification: Demystify & Align provides information on veteran certification and opportunities for improvement and coordination

Fast Crash Recovery in RAMCloud

RAMCloud is a DRAM-based storage system that provides inexpensive durability and availability by recovering quickly after crashes, rather than storing replicas in DRAM. RAMCloud scatters backup data across hundreds or thousands of disks, and it harnesses hundreds of servers in parallel to reconstruct lost data. The system uses a log-structured approach for all its data, in DRAM as well as on disk; this provides high performance both during normal operation and during recovery. RAMCloud employs randomized techniques to manage the system in a scalable and decentralized fashion. In a 60-node cluster, RAMCloud recovers 35 GB of data from a failed server in 1.6 seconds. Our measurements suggest that the approach will scale to recover larger memory sizes (64 GB or more) in less time with larger clusters

Reducing the Frequency of Data Loss in Cloud Storage using Copysets”,

ABSTRACT Random replication is widely used in data center storage systems to prevent data loss. However, random replication is almost guaranteed to lose data in the common scenario of simultaneous node failures due to cluster-wide power outages. Due to the high fixed cost of each incident of data loss, many data center operators prefer to minimize the frequency of such events at the expense of losing more data in each event. We present Copyset Replication, a novel generalpurpose replication technique that significantly reduces the frequency of data loss events. We implemented and evaluated Copyset Replication on two open source data center storage systems, HDFS and RAMCloud, and show it incurs a low overhead on all operations. Such systems require that each node's data be scattered across several nodes for parallel data recovery and access. Copyset Replication presents a near optimal tradeoff between the number of nodes on which the data is scattered and the probability of data loss. For example, in a 5000-node RAMCloud cluster under a power outage, Copyset Replication reduces the probability of data loss from 99.99% to 0.15%. For Facebook's HDFS cluster, it reduces the probability from 22.8% to 0.78%

The Case for RAMClouds: Scalable High-Performance Storage Entirely in DRAM

Disk-oriented approaches to online storage are becoming increasingly problematic: they do not scale gracefully to meet the needs of large-scale Web applications, and improvements in disk capacity have far outstripped improvements in access latency and bandwidth. This paper argues for a new approach to datacenter storage called RAMCloud, where information is kept entirely in DRAM and large-scale systems are created by aggregating the main memories of thousands of commodity servers. We believe that RAMClouds can provide durable and available storage with 100-1000x the throughput of disk-based systems and 100-1000x lower access latency. The combination of low latency and large scale will enable a new breed of dataintensive applications.