Sentence examples for thin datasets from inspiring English sources

Some practices contribute data to both GPRD and to THIN and so potentially some patients with chickenpox and stroke could have been duplicated in the GPRD and THIN datasets.

A cohort of patients was selected from The Health Improvement Network (THIN) dataset of anonymised patient-level data from UK general practice with a confirmed chronic rheumatic diagnosis.

We identified GPRD/THIN duplicated cases on the basis of year of birth, sex, geographical region of the practice, and dates of chickenpox and stroke, and excluded these cases from the THIN dataset.

The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model.

Reclassification of ground and canopy returns in the thinned datasets resulted in a larger fraction of points being classified as ground returns after thinning.

We thinned this dataset to the approximate SNP density and MAF distribution observed in real Affymetrix 500k data on a set of British controls [the 1958 Birth Cohort of the Wellcome Trust Case Control Consortium (2007)].

Because background linkage disequilibrium (LD) can affect both principal component and structure-like analysis [ 96], we thinned the dataset by removing one SNP of a pair in strong LD (genotypic correlation r>0.4) in a window of 200 SNPs (sliding the window by 25 SNPs at a time).

In order to estimate the effect of point density on the metrics we thinned the original datasets to 10 points m−2 (SNM and SdM), 5 and 1 point m−2.

Computer-assisted diagnosis (CAD) based on thin-section CT datasets is about to be introduced into clinical routine not only for nodule volumetry but also for assessment of enhancement and detection of nodules.

We have, therefore, used Mosaic, alongside the Townsend Index, to examine smoking prevalence within patients in a large primary care dataset, The Health Improvement Network (THIN)[ 8].

Amongst patients in the large primary care dataset of THIN, we have shown clear socioeconomic differences in smoking prevalence according to both of these measures.