Building a progression culture: exploring learning organisations’ use of the Progression Matrix

This research paper explores the implementation of The Progression Matrix in schools, colleges and other learning organisations such as training providers. The project builds on existing research on The Progression Matrix and finds evidence which suggests that the approach provides a useful conceptual model around which learning organisations can re-orientate their practice and deliver enhanced progression for learners.Aimhighe

The impact of phenotype, ethnicity and genotype on progression of Type 2 diabetes mellitus

Aim: To conduct a comprehensive review of studies of glycaemic deterioration in type 2 diabetes and identify the major factors influencing progression.Methods: We conducted a systematic literature search with terms linked to type 2 diabetes progression. All the included studies were summarized based upon the factors associated with diabetes progression and how the diabetes progression was defined.Results: Our search yielded 2785 articles; based on title, abstract and full‐text review, we included 61 studies in the review. We identified seven criteria for diabetes progression: ‘Initiation of insulin’, ‘Initiation of oral antidiabetic drug’, ‘treatment intensification’, ‘antidiabetic therapy failure’, ‘glycaemic deterioration’, ‘decline in beta‐cell function’ and ‘change in insulin dose’. The determinants of diabetes progression were grouped into phenotypic, ethnicity and genotypic factors. Younger age, poorer glycaemia and higher body mass index at diabetes diagnosis were the main phenotypic factors associated with rapid progression. The effect of genotypic factors on progression was assessed using polygenic risk scores (PRS); a PRS constructed from the genetic variants linked to insulin resistance was associated with rapid glycaemic deterioration. The evidence of impact of ethnicity on progression was inconclusive due to the small number of multi‐ethnic studies.Conclusion: We have identified the major determinants of diabetes progression—younger age, higher BMI, higher HbA1c and genetic insulin resistance. The impact of ethnicity is uncertain; there is a clear need for more large‐scale studies of diabetes progression in different ethnic groups

Progression of apprentices to higher education

This report presents the findings of research undertaken for the Department for Business, Innovation and Skills (BIS) into the progression to higher education of advanced level apprentices over the past seven years. This is part of a longitudinal study whose first results were published in 2011 (Joslin & Smith, 2011)

Risk Factors for Long-Term Coronary Artery Calcium Progression in the Multi-Ethnic Study of Atherosclerosis.

BackgroundCoronary artery calcium (CAC) detected by noncontrast cardiac computed tomography scanning is a measure of coronary atherosclerosis burden. Increasing CAC levels have been strongly associated with increased coronary events. Prior studies of cardiovascular disease risk factors and CAC progression have been limited by short follow-up or restricted to patients with advanced disease.Methods and resultsWe examined cardiovascular disease risk factors and CAC progression in a prospective multiethnic cohort study. CAC was measured 1 to 4 times (mean 2.5 scans) over 10 years in 6810 adults without preexisting cardiovascular disease. Mean CAC progression was 23.9 Agatston units/year. An innovative application of mixed-effects models investigated associations between cardiovascular disease risk factors and CAC progression. This approach adjusted for time-varying factors, was flexible with respect to follow-up time and number of observations per participant, and allowed simultaneous control of factors associated with both baseline CAC and CAC progression. Models included age, sex, study site, scanner type, and race/ethnicity. Associations were observed between CAC progression and age (14.2 Agatston units/year per 10 years [95% CI 13.0 to 15.5]), male sex (17.8 Agatston units/year [95% CI 15.3 to 20.3]), hypertension (13.8 Agatston units/year [95% CI 11.2 to 16.5]), diabetes (31.3 Agatston units/year [95% CI 27.4 to 35.3]), and other factors.ConclusionsCAC progression analyzed over 10 years of follow-up, with a novel analytical approach, demonstrated strong relationships with risk factors for incident cardiovascular events. Longitudinal CAC progression analyzed in this framework can be used to evaluate novel cardiovascular risk factors

Leveraging Disease Progression Learning for Medical Image Recognition

Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of a disease may continuously follow a certain progression pattern. In this paper, we propose a novel method that leverages disease progression learning for medical image recognition. In our method, sequences of images ordered by disease stages are learned by a neural network that consists of a shared vision model for feature extraction and a long short-term memory network for the learning of stage sequences. Auxiliary vision outputs are also included to capture stage features that tend to be discrete along the disease progression. Our proposed method is evaluated on a public diabetic retinopathy dataset, and achieves about 3.3% improvement in disease staging accuracy, compared to the baseline method that does not use disease progression learning