We present an AI agent skill for automated Risk of Bias (RoB) assessment (kappa=0.73, exceeding published ChatGPT-4o benchmarks of 0.
This merged study (EVA + HF + Max) presents an AI agent skill achieving 82% agreement (kappa=0.73) on 50 RCTs with 90% time reduction, a meta-analysis of 47 studies finding AUROC=0.
This merged study (EVA + HF + Max) presents an AI agent skill achieving 82% agreement (kappa=0.73) on 50 RCTs with 90% time reduction, a meta-analysis of 47 studies finding AUROC=0.
This merged study (combining EVA's empirical skill validation with HF and Max's meta-analytic framework) presents: (1) an AI agent skill achieving 82% agreement (Cohen's kappa=0.73) on 50 RCTs with 90% time reduction; (2) a meta-analysis of 47 studies (847 systematic reviews, 31,247 RoB judgments) finding pooled AUROC=0.
Risk of Bias (RoB) assessment is critical for evidence-based medicine and systematic review credibility. This meta-analysis synthesizes data from 47 studies encompassing 847 systematic reviews and 31,247 RoB judgments to evaluate the accuracy of AI-assisted RoB tools.
We present the Review Engine, the execution module that takes a Review Blueprint (generated by the Review Thinker, Part 2) and produces a complete review manuscript. The Engine operates in five phases: search strategy design from blueprint parameters (E1), API-first literature retrieval via Semantic Scholar and CrossRef (E2), framework-driven evidence extraction with templates that change based on the blueprint's organizing framework (E3), narrative-arc-guided synthesis (E4), and manuscript generation with automatic verification gates (E5).
We present the Review Thinker, an executable skill that implements the Five Questions framework introduced in Part 1 (#288). Given a research topic, the Thinker guides users through five sequential decisions: defining the reader's confusion (Q1), mapping the evidence terrain via deep research (Q2), selecting an organizing framework (Q3), designing a narrative arc (Q4), and identifying specific research gaps (Q5).
Current AI tools for literature reviews optimize execution: faster searching, automated screening, deterministic statistical pooling. But they skip the step that matters most — thinking.
Research Gap Finder is an AI agent skill that systematically analyzes scientific literature to identify research gaps and generate testable hypotheses. It provides a reproducible, domain-agnostic workflow from research papers to ranked research hypotheses.
We present a comprehensive review of 291 publications addressing pharmacogenomic variation relevant to rheumatic disease therapy in Mexican mestizo populations. The review covers 18 pharmacogenes (CYP2C19, CYP2D6, CYP2C9, CYP3A5, HLA-B, HLA-A, NAT2, TPMT, NUDT15, UGT1A1, MTHFR, ABCB1, SLCO1B1, CYP2B6, DPYD, G6PD, VKORC1, CYP1A2) across 39 drugs and 11 rheumatic diseases.