Publications by authors named "Rebecca Woodfield"
Article Synopsis
- The study investigated the accuracy of stroke identification methods in the UK Biobank, using genetic data to validate coding systems.
- The researchers created 12 different stroke definitions based on various medical codes and self-reports, analyzing data from over 408,000 participants.
- Results showed significant genetic correlations across all definitions compared to the MEGASTROKE study, highlighting variability in stroke case numbers related to coding sources while confirming some known genetic loci associated with stroke.
Objective: In UK Biobank (UKB), a large population-based prospective study, cases of many diseases are ascertained through linkage to routinely collected, coded national health datasets. We assessed the accuracy of these for identifying incident strokes.
Methods: In a regional UKB subpopulation (n = 17,249), we identified all participants with ≥1 code signifying a first stroke after recruitment (incident stroke-coded cases) in linked hospital admission, primary care, or death record data.
Background: Motor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets.
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- Accurately classifying non-traumatic intracerebral hemorrhage (ICH) subtypes is crucial due to differences in risk factors, management, and outcomes, prompting a study on reliability of classification systems.
- The review included 8 studies, revealing substantial to perfect reliability for anatomical and mechanistic systems through various kappa values, though quality of reporting showed variability and potential biases.
- While classification reliability seems strong, it’s based on experienced raters in specialized centers; future studies should adhere to new guidelines to improve reliability comparisons.
Objective: Long-term follow-up of population-based prospective studies is often achieved through linkages to coded regional or national health care data. Our knowledge of the accuracy of such data is incomplete. To inform methods for identifying stroke cases in UK Biobank (a prospective study of 503,000 UK adults recruited in middle-age), we systematically evaluated the accuracy of these data for stroke and its main pathological types (ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage), determining the optimum codes for case identification.
View Article and Find Full Text PDFObjective: We performed a systematic review of the accuracy of patient self-report of stroke to inform approaches to ascertaining and confirming stroke cases in large prospective studies.
Methods: We sought studies comparing patient self-report against a reference standard for stroke. We extracted data on survey method(s), response rates, participant characteristics, the reference standard used, and the positive predictive value (PPV) of self-report.
Background And Purpose: NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium.
Methods: Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System.