Meta-analysis and systematic reviews

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

The paper discusses methods to standardize effect sizes in fMRI data and presents approaches for meta-analyzing results across studies.

The paper reviews neuroscience publications to assess hypothesis-testing practices and shows that combining multiple test results increases predicted research reliability.

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.

The paper develops a tool to assess robustness against publication bias in fMRI meta-analyses by simulating noise studies and testing parameter effects.

The paper presents a meta-analytic approach to combine variance ratios from different studies to produce more robust estimates, addressing the issue of wide confidence intervals in small studies.