ArticleJB & JS open access
A Locally Executed Agentic Artificial Intelligence Framework for Deduplication, Screening, and Structured Data Extraction in Spine Surgery Systematic Reviews: A PRISMA-Transparent Reporting of Artificial Intelligence in Comprehensive Evidence Synthesis-Compliant Evaluation.
Article in JB & JS open access. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systematic reviews and meta-analyses remain time-consuming and labor-intensive. We developed and validated a locally executed agentic artificial intelligence (AI) framework for deduplication, screening, and structured data extraction in systematic reviews, reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-Transparent Reporting of AI in Comprehensive Evidence Synthesis (trAIce) guidelines. A multiagent pipeline of specialized agents for deduplication, title/abstract screening, structured data extraction, and verification was executed entirely locally to ensure data governance and reproducibility. Human-in-the-loop validation compared outputs against dual independent reviewers across 3 spine surgery systematic reviews, assessing accuracy, inter-rater agreement (Cohen's κ), time savings, and clinically critical error rates. Across 6,214 records, deduplication achieved near-perfect agreement with human reviewers (κ = 0.98), title and abstract screening yielded higher concordance than human screening (κ = 0.91) while reducing full-text review volume by 83%, and structured data extraction reached substantial agreement (κ = 0.87). The framework reduced reviewer time by 91.1% (90.8%-91.4%), a mean saving of 19.5 hours per review (p < 0.001). Clinically critical discrepancies were rare (<1%) and traceable, with no fabricated or hallucinated data introduced. A locally executed agentic AI framework has the potential to deliver accurate, efficient, and secure automation of systematic review tasks with human oversight, offering a reproducible pathway for trustworthy evidence synthesis under PRISMA-trAIce standards.
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.