Dataset Information

Characterization of the peer review network at the Center for Scientific Review, National Institutes of Health.

ABSTRACT: The National Institutes of Health (NIH) is the largest source of funding for biomedical research in the world. This funding is largely effected through a competitive grants process. Each year the Center for Scientific Review (CSR) at NIH manages the evaluation, by peer review, of more than 55,000 grant applications. A relevant management question is how this scientific evaluation system, supported by finite resources, could be continuously evaluated and improved for maximal benefit to the scientific community and the taxpaying public. Towards this purpose, we have created the first system-level description of peer review at CSR by applying text analysis, bibliometric, and graph visualization techniques to administrative records. We identify otherwise latent relationships across scientific clusters, which in turn suggest opportunities for structural reorganization of the system based on expert evaluation. Such studies support the creation of monitoring tools and provide transparency and knowledge to stakeholders.

SUBMITTER: Boyack KW

PROVIDER: S-EPMC4132088 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterization of the peer review network at the Center for Scientific Review, National Institutes of Health.

Boyack Kevin W KW Chen Mei-Ching MC Chacko George G

PloS one 20140813 8

The National Institutes of Health (NIH) is the largest source of funding for biomedical research in the world. This funding is largely effected through a competitive grants process. Each year the Center for Scientific Review (CSR) at NIH manages the evaluation, by peer review, of more than 55,000 grant applications. A relevant management question is how this scientific evaluation system, supported by finite resources, could be continuously evaluated and improved for maximal benefit to the scient ...[more]

PMID: 25119140

Similar Datasets

Project description:BackgroundReadmissions are a common problem in cardiac surgery. The goal of this study was to examine the frequency, timing, and associated risk factors for readmission after cardiac operations.MethodsA 10-center cohort study prospectively enrolled 5,158 adult cardiac surgical patients (5,059 included in analysis) to assess risk factors for infection after cardiac operations. Data were also collected on all-cause readmissions occurring within 65 days after the operation. Major outcomes included the readmission rate stratified by procedure type, cause of readmission, length of readmission stay, and discharge disposition after readmission. Multivariable Cox regression was used to determine risk factors for time to first readmission.ResultsThe overall rate of readmission was 18.7% (number of readmissions, 945). When stratified by the most common procedure type, readmission rates were isolated coronary artery bypass grafting, 14.9% (n = 248); isolated valve, 18.3% (n = 337); and coronary artery bypass grafting plus valve, 25.0% (n = 169). The three most common causes of first readmission within 30 days were infection (17.1% [n = 115]), arrhythmia (17.1% [n = 115]), and volume overload (13.5% [n = 91]). More first readmissions occurred within 30 days (80.6% [n = 672]) than after 30 days (19.4% [n = 162]), and 50% of patients were readmitted within 22 days from the index operation. The median length of stay during the first readmission was 5 days. Discharge in 15.8% of readmitted patients (n = 128) was to a location other than home. Baseline patient characteristics associated with readmission included female gender, diabetes mellitus on medication, chronic obstructive pulmonary disease, elevated creatinine, lower hemoglobin, and longer operation time. More complex surgical procedures were associated with an increased risk of readmission compared with the coronary artery bypass grafting group.ConclusionsNearly 1 of 5 patients who undergo cardiac operations require readmission, an outcome with significant health and economic implications. Management practices to avert in-hospital infections, reduce postoperative arrhythmias, and avoid volume overload offer important targets for quality improvement.

Dataset Information

Characterization of the peer review network at the Center for Scientific Review, National Institutes of Health.

Publications

Characterization of the peer review network at the Center for Scientific Review, National Institutes of Health.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets