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Background <p>Identifying confounding variables is fundamental for robust observational studies, yet the traditional manual process is a time-consuming and subjective barrier for researchers. Recent advances in Retrieval-Augmented Generation (RAG) offer a promising solution, but most existing systems rely on full-text access, cloud-hosted APIs, or manually curated knowledge graphs, raising concerns about privacy, copyright, and computational cost, and making local deployment difficult.</p> Objective <p>This study developed and evaluated a heuristic tool to scope candidate confounders for adjustment in observational studies. Using a locally deployed, abstract-only RAG architecture, our tool generates a traceable shortlist of candidate confounders from PICO (Population, Intervention, Comparison, Outcome) queries over medical abstracts.</p> Methods <p>We implemented a three-stage architecture for PICO-based scoping of candidate confounder. The pipeline was deployed on an all-in-one local server and evaluated using 1,000 expert-curated PICO queries spanning 20 clinical specialties. Performance was assessed along four dimensions—internal consistency, output volume, efficiency, and clinical acceptance—by a multi-institutional clinician panel, and was compared with a graph-only SemMedDB baseline.</p> Results <p>Across repeated runs, the pipeline showed high internal consistency (candidate confounder list consistency 94.6%±8.7%; PMID set consistency 79.4%±23.5%). It suggested a median of 6 candidate confounders (IQR 8) for adjustment and retrieved a median of 33 unique PMIDs (IQR 7) per query. Median processing time was 44.50&#xa0;s (IQR 31.72). Expert review yielded an overall clinical acceptance rate of 87.12%.</p> Conclusions <p>In an exploratory capacity, a locally deployed, abstract-only RAG workflow can generate clinically interpretable and traceable candidate confounder suggestions to support early-stage observational study design, particularly in settings with privacy constraints or limited access to full texts and cloud resources.</p> Trial registration <p>NA.</p>

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Automated candidate confounder scoping for adjustment in clinical research: a retrieval-augmented generation approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Jingjing Li,
  • Kesong Wu,
  • Xiao Wang,
  • Ruolin Tao,
  • Yinyao Ma,
  • Hanlin Lv,
  • Xueqian Zhao,
  • Yu Lan,
  • Lei Li,
  • Lei Wang

摘要

Background

Identifying confounding variables is fundamental for robust observational studies, yet the traditional manual process is a time-consuming and subjective barrier for researchers. Recent advances in Retrieval-Augmented Generation (RAG) offer a promising solution, but most existing systems rely on full-text access, cloud-hosted APIs, or manually curated knowledge graphs, raising concerns about privacy, copyright, and computational cost, and making local deployment difficult.

Objective

This study developed and evaluated a heuristic tool to scope candidate confounders for adjustment in observational studies. Using a locally deployed, abstract-only RAG architecture, our tool generates a traceable shortlist of candidate confounders from PICO (Population, Intervention, Comparison, Outcome) queries over medical abstracts.

Methods

We implemented a three-stage architecture for PICO-based scoping of candidate confounder. The pipeline was deployed on an all-in-one local server and evaluated using 1,000 expert-curated PICO queries spanning 20 clinical specialties. Performance was assessed along four dimensions—internal consistency, output volume, efficiency, and clinical acceptance—by a multi-institutional clinician panel, and was compared with a graph-only SemMedDB baseline.

Results

Across repeated runs, the pipeline showed high internal consistency (candidate confounder list consistency 94.6%±8.7%; PMID set consistency 79.4%±23.5%). It suggested a median of 6 candidate confounders (IQR 8) for adjustment and retrieved a median of 33 unique PMIDs (IQR 7) per query. Median processing time was 44.50 s (IQR 31.72). Expert review yielded an overall clinical acceptance rate of 87.12%.

Conclusions

In an exploratory capacity, a locally deployed, abstract-only RAG workflow can generate clinically interpretable and traceable candidate confounder suggestions to support early-stage observational study design, particularly in settings with privacy constraints or limited access to full texts and cloud resources.

Trial registration

NA.