How it works

How Kurate grades evidence.

Kurate reads a curated evidence base, grades papers with our own quality system, and uses that signal when answering clinical research questions. We show conclusions and provenance without turning the page into an implementation manual.

01

Read sources

We work from curated papers, extracted metadata, reports, and source-linked validation artifacts.

02

Apply grading

Kurate considers study design, reporting quality, statistics, registration, and provenance.

03

Answer carefully

Stronger findings are prioritized; weak, conflicting, or incomplete evidence stays visible with caveats.

Our grading system

Kurate uses a proprietary evidence grading system for source-grounded review. It is not a citation counter and it is not a replacement for clinical judgement.

Similar traditions already exist. GRADE, for example, is widely used in evidence-based medicine. At a high level, frameworks like GRADE consider risk of bias, inconsistency, indirectness, imprecision, and publication bias.

Built by experts

Kurate is built by experts who publish in statistics, research methodology, and metascience. That background shapes how we handle fragile claims, uncertain comparisons, and noisy literature.

Matthew Vowels

CTO, Kivira HealthPhD Eng.PhD Appl. Math.

Research in causal inference, deep generative modelling, and multimodal ML. 50+ peer-reviewed publications including ICLR and NeurIPS.

Two PhDs: one in Engineering (Vision, Speech & Signal Processing) from the University of Surrey, and one in Applied Mathematics for Human & Social Sciences from the University of Lausanne. Research affiliations with University of Lausanne, University of Surrey, and the Sense Center for Innovation and Research.

Jamie Cummins

PhDUniversity of Bern

Research collaborator and domain expert contributing to the Kurate evidence evaluation methodology and clinical rubric development.

Expertise in research integrity assessment and LLM-workflow evaluation.

Selected related publications

Methodological and meta-scientific work associated with the Kurate team.

  1. Vowels, M. J. (2023). Misspecification and unreliable interpretations in psychology and social science. Psychological Methods, 28(3), 507-526. DOI
  2. Vowels, M. J., Vowels, L. M., & Wood, N. D. (2023). Spectral and cross-spectral analysis: A tutorial for psychologists and social scientists. Psychological Methods, 28(3), 631-650. DOI
  3. Vowels, M. J. (2023). Prespecification of structure for the optimization of data collection and analysis. Collabra: Psychology, 9(1), Article 71300. DOI
  4. Vowels, M. J. (2024). Trying to outrun causality with machine learning: Limitations of model explainability techniques for exploratory research. Psychological Methods. DOI
  5. Vowels, M. J. (2025). A causal research pipeline and tutorial for psychologists and social scientists. Psychological Methods. DOI
  6. Vowels, M. J. (2024). Typical yet unlikely and normally abnormal: The intuition behind high-dimensional statistics. Statistics, Politics and Policy, 15(1), 87-113. DOI
  7. Aczel, B., Szaszi, B., Clelland, H. T., Kovacs, M., Holzmeister, F., et al. (2026). Investigating the analytical robustness of the social and behavioural sciences. Nature, 652(8108), 135-142. DOI
  8. Higgins, W. C., Clarke, B., Elson, M., & Cummins, J. (2026). Recommendations for incorporating LLMs into psychological research: A commentary on Austin and colleagues (2026). PsyArXiv. DOI
  9. Ahnström, L., Bruckner, T., Aspromonti, D. A., Caquelin, L., Cummins, J., et al. (2026). TrialScout links published results to trial registrations using a large language model. medRxiv. DOI
  10. Elson, M., Hussey, I., Clarke, B., Norwood, S. F., Grinschgl, S., Arslan, R. C., et al. (2026). Against anonymising meta-scientific data. PsyArXiv. DOI
  11. Cummins, J., Clarke, B., Hussey, I., & Elson, M. (2026). RegCheck: A tool for automating comparisons between study registrations and papers. arXiv. DOI
  12. Miske, O., Abatayo, A. L., Daley, M., Dirzo, M., Fox, N., Haber, N., Hahn, K. M., et al. (2026). Investigating the reproducibility of the social and behavioural sciences. Nature, 652(8108), 126-134. DOI
  13. Cummins, J. (2025, September 1). Psychology needs... an AI revolution. The Psychologist. Article
  14. Röseler, L., Kaiser, L., Doetsch, C., Klett, N., Seida, C., Schütz, A., Aczel, B., et al. (2024). The Replication Database: Documenting the replicability of psychological science. Journal of Open Psychology Data, 12(1), Article 8. DOI
  15. Tierney, W., Hardy, J. H., III, Ebersole, C. R., Leavitt, K., Viganola, D., Clemente, E. G., Gordon, M., Dreber, A., Johannesson, M., Pfeiffer, T., Hiring Decisions Forecasting Collaboration, & Uhlmann, E. L. (2020). Creative destruction in science. Organizational Behavior and Human Decision Processes, 161, 291-309. DOI
  16. Van Dessel, P., Cummins, J., Hughes, S. J., Kasran, S., Cathelyn, F., & Moran Yorovich, T. (2020). Reflecting on twenty-five years of research using implicit measures: Recommendations for their future use. Social Cognition, 38(Supplement), S223-S242. DOI