Malcolm Robert Macleod

ASySD is a tool that efficiently removes duplicate citations in biomedical systematic reviews with high sensitivity and reliability.

The paper examines rare variants in monogenic cerebral small vessel disease genes in UK Biobank participants, finding limited phenotype associations and suggesting issues with previous gene-phenotype links.

This paper applies natural language processing to assess risk of bias in preclinical literature, achieving high F1 scores for several bias items using neural models and BERT.

This paper argues for embracing heterogeneity in preclinical research to improve reproducibility and generalizability through meta-analysis of phenotypic variability in ischaemic stroke models.

The paper develops machine learning classifiers to automate citation screening in pre-clinical systematic reviews, achieving high sensitivity and specificity across different domains.

This paper applies machine learning to citation screening in systematic reviews of animal studies, improving sensitivity and identifying human errors.

This study reviews effect sizes and statistical power in rodent fear conditioning research, finding low power and inconsistent reporting of these metrics.

This paper compares statistical methods for meta-analysis of animal studies, finding that normalized mean difference (NMD) has higher power than standardized mean difference (SMD), and meta-regression outperforms stratified meta-analysis.

This study found that a change in Nature journals' editorial policy improved reporting of bias-reducing measures in in vivo research, but not in vitro research.