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At least 847 records · Page 47Linked to original sources
PubMed2001

Chairpersons of pathology in the United States. Benchmarks for academic publications and professional credentials.

Chairpersons of pathology often are viewed as departmental role models in academic medical centers. To objectify this view, we undertook a systematic survey of publication records and professional certification among 126 chairpersons in the United States. The median of the total number of scientific publications by the cohort was 105 since graduation from medical school, and the median yearly number of peer-reviewed papers was 3.34 per person (mean, 4.25). A random 10% of the study population was analyzed further with reference to the percentage of publications that reflected basic science research; 41% of the total literature contributions of this subgroup fit that description, and only 38% of the chairpersons in the subgroup had 80% or more non-service-related publications. Of all chairpersons, 85% had obtained primary board certification in anatomic pathology, clinical pathology, or both, and 25% of the group had earned at least 1 subspecialty board certificate in addition. These numbers reflect an evolution in the professional backgrounds of chairpersons of pathology such that demands for academic scholarship and proficiency in hospital practice and management seem to pertain to that group.

Benchmarking↗
PubMed2005

Quality assessment of dyslipidemia in managed care: current best evidence should be used to benchmark quality.

Quality measurements in managed care allow purchasers of health care to distinguish between health plans. Existing measures (Health Plan Employer Data and Information Set goals) for treatment of dyslipidemia provide a limited snapshot about quality of care for members within commercial health plans. Newer evidence (ie, the Heart Protection Study) and consensus guidelines (the National Cholesterol Education Program) expand the definition of high-risk populations and emphasize pharmacotherapy in managing dyslipidemia. We believe that newer evidence and standards provide health plans with the best opportunity to accurately assess the quality of dyslipidemia care for their populations. We propose a broad framework that provides health plans with guidance on developing a new quality measure for dyslipidemia that focuses on pharmacotherapy.

Adult↗
PubMed2026

A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.

In recent years, high dimensional omics analyses have become more commonplace for investigating complex biological systems. Typically, these studies attempt to identify key biomolecules associated with a particular biological process. Often, machine learning (ML) is used to identify these biomolecules, typically by learning which biomolecules are highly predictive of a treatment, biological outcome, or phenotype. A major challenge of applying ML to high throughput omics is overcoming noise when sample size is limited and unbalanced with respect to tens of thousands of biomolecules measured. Thus, feature selection (the process of reducing the number of predictors) is both a critical and common step in the ML analysis pipeline. While much attention has been given to embedding and wrapping techniques for feature selection in the omics space, filter-based methods for model-free feature selection have appealing theoretical properties. This manuscript evaluates sure screening, a class of filter-based feature selection methods which provide analytical guarantees for true feature set retention. Here, we cover existing feature screening methods based on the sure screening principal, available software, methods to improve feature screening, and contextualize feature screening in the larger discussion of feature selection for omics data analysis. Additionally, a suite of model-free sure screening approaches is applied and compared for several omics biomedical applications in a ML classification context. We identified BcorSIS as the most effective and computationally efficient screening method across various omics datasets, consistently outperforming others like CSIS and DCSIS in runtime.

Humans↗
PubMed2004

Tertiary structure predictions on a comprehensive benchmark of medium to large size proteins.

We evaluate tertiary structure predictions on medium to large size proteins by TASSER, a new algorithm that assembles protein structures through rearranging the rigid fragments from threading templates guided by a reduced Calpha and side-chain based potential consistent with threading based tertiary restraints. Predictions were generated for 745 proteins 201-300 residues in length that cover the Protein Data Bank (PDB) at the level of 35% sequence identity. With homologous proteins excluded, in 365 cases, the templates identified by our threading program, PROSPECTOR_3, have a root-mean-square deviation (RMSD) to native < 6.5 angstroms, with >70% alignment coverage. After TASSER assembly, in 408 cases the best of the top five full-length models has a RMSD < 6.5 angstroms. Among the 745 targets are 18 membrane proteins, with one-third having a predicted RMSD < 5.5 A. For all representative proteins less than or equal to 300 residues that have corresponding multiple NMR structures in the Protein Data Bank, approximately 20% of the models generated by TASSER are closer to the NMR structure centroid than the farthest individual NMR model. These results suggest that reasonable structure predictions for nonhomologous large size proteins can be automatically generated on a proteomic scale, and the application of this approach to structural as well as functional genomics represent promising applications of TASSER.

Algorithms↗