ReviewMayo Clinic proceedings. Digital health2024
Future of Evidence Synthesis: Automated, Living, and Interactive Systematic Reviews and Meta-analyses.
Review in Mayo Clinic proceedings. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative Survival in Metastatic Hormone-sensitive Prostate Cancer by Volume of Disease and Timing of Metastasis: A Living Network Meta-analysis.European urology · 2026Pooled it
- MES: A Multi-Agent Evidence Synthesis System for Medical Decision-Making.medRxiv : the preprint server for health sciences · 2026Article
- Ensuring trustworthy AI assisted guideline development for clinical practice.NPJ digital medicine · 2026Article
- A Machine Learning and Large Language Model Tool for Systematic Literature Reviews of Health Economic Evidence: A Validation Study.PharmacoEconomics · 2026Article
- Integrating Artificial Intelligence and Automation Into Living Guideline Development: A Scoping Review of Existing Frameworks, Methods, and Applications.Clinical and public health guidelines · 2026Article
- Collaborative large language models (LLMs) are all you need for screening in systematic reviews.medRxiv : the preprint server for health sciences · 2026Article
- Automating the data extraction process for systematic reviews using GPT-4o and o3.Research synthesis methods · 2026Article
- Comparison of three large language models' ability to assess the risk of bias using ROBINS-I tool.BMJ digital health & AI · 2026Article
- Streamlining evidence based clinical recommendations with large language models.NPJ digital medicine · 2025Article
- Lifecycles of Cochrane Systematic Reviews (2003-2024): A Bibliographic Study.Cochrane evidence synthesis and methods · 2025Article
- Artificial Intelligence and Automation in Evidence Synthesis: An Investigation of Methods Employed in Cochrane, Campbell Collaboration, and Environmental Evidence Reviews.Cochrane evidence synthesis and methods · 2025Article
- CLEAR: A vision to support clinical evidence lifecycle with continuous learning.Journal of biomedical informatics · 2025Article
- Evidence triangulator: using large language models to extract and synthesize causal evidence across study designs.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.