Evidence Synthesis
Evidence synthesis brings findings from multiple studies together to answer a research question while retaining differences, limitations, and uncertainty.
Evidence synthesis brings findings from multiple studies together to answer a question. Cochrane's explainer describes this in health research; the general idea also applies to other research questions. Cochrane
A single-paper summary explains one report. A synthesis considers findings together while retaining their differences and uncertainty. A narrative synthesis can discuss those relationships without forcing one average. A systematic review requires an explicit review method; some reviews also include statistical meta-analysis. Those labels do not follow automatically from using AI.
An illustrative comparison
Consider a fictional workplace-learning example, not real research findings. One selected study measures onboarding task completion; another records interviews about the experience. The first can address the measured outcome, while the second supplies a different kind of observation.
A useful synthesis explains which question each can answer and what remains unknown. It should not count two favorable summaries as interchangeable votes or turn interview impressions into a measured effect. Multiple reports of one study also do not become independent studies.
AI's role
AI can help organize supplied evidence into a proposed table or comparison. Review the search record, eligibility judgments, original sources, and missing fields before accepting the written conclusion. A polished brief cannot establish that the search was complete.
The AI literature-review workflow produces a bounded brief with those records. Elicit is a research-workflow option to evaluate. The Research Evidence Extractor works from selected supplied sources; the Literature Screening Reviewer audits proposed eligibility judgments. Their separate jobs help preserve the path from source selection to conclusion.
Filed under
evidence-synthesis · knowledge-work
Related
- AI Literature Review Workflow: Screen, Extract, and VerifyUse AI for a literature review with explicit eligibility criteria, study-level evidence tables, source locations, and checks for missing or conflicting data.
- ElicitElicit supports academic research with a Research Agent, research reports, and a structured workflow for screening papers and extracting evidence.
- Literature Screening ReviewerUse this agent to audit a literature screening log against a supplied protocol and local abstracts or paper extracts: trace each eligibility judgment to criteria and source text, flag unsupported exclusions, identify unresolved eligibility, and record protocol drift. Review the log without searching the web or changing inclusion decisions.
- Research Evidence ExtractorExtract a structured study evidence table from supplied paper text or readable research excerpts using a user-defined column schema: record study and source IDs, methods, population, outcomes, units, time points, source locations, and missing data. Use after papers have been selected and before synthesis; do not search for papers, decide eligibility, compute pooled effects, or draft conclusions.
- Check Evidence TableCheck a study evidence CSV against a source registry with code: validate required columns and cells, row IDs, source references, duplicate study/outcome/time-point keys, and allowed status values, then report structural errors without judging research claims.