The University of Edinburgh
Co-Authors: Emily S Sena, Malcolm R Macleod
A Living Systematic Review of Alzhemer’s Disease Studies
Alzheimer’s disease (AD) is a devastating, incurable neurodegenerative disorder. Systematic reviews of the in vivo literature have identified poor methodological rigor and low reporting of measures to mitigate risks of bias. Such shortcomings can lead to overestimates of treatment effect, threaten reproducibility, and contribute to translational failure. We aim to create a “living” framework for the continual synthesis and quality assessment of in vivo AD experiments using automation tools. We screened a sample of papers to provide instances to train a machine learning algorithm, which could then detect if a paper contained primary research in transgenic AD animal models. Using validated text-mining approaches, we will assess reporting quality and classify studies based on models, treatments, and outcome measures. We anticipate that the development of a curated, online database will accelerate meta-research, and provide an important resource for stakeholders in AD research.