Michelle Vaccaro

Artificial Intelligence · 2024

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Michelle Vaccaro trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Mike W.-L. Cheung
  2. W. Viechtbauer
  3. J. Sterne
  4. Julian P. T. Higgins
  5. David Moher
  6. Yunfeng Zhang
  7. J. Peters
  8. Matthias Egger
  9. A. Liberati
  10. Q. Liao

Papers from Michelle Vaccaro's perspective

Ordered by how strongly the researchers Michelle Vaccaro trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
  2. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. (Journal of Clinical Epidemiology)
  3. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. (International Journal of Surgery)
  4. Outlier and influence diagnostics for meta‐analysis (Research Synthesis Methods)
  5. Controlling the risk of spurious findings from meta‐regression (Statistics in Medicine)
  6. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. (Journal of Clinical Epidemiology)
  7. Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach. (Psychological methods)
  8. Choosing effect measures and computing estimates of effect (Cochrane Handbook for Systematic Reviews of Interventions)
  9. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS
  10. Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias (PLoS ONE)

Their papers