lymphoma-TLS-outcome

Orchestrate agent team for meta-analysis pipeline. Creates team, spawns teammates, manages task list and handoffs between pipeline stages.

Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis. Use when moving from full-text collection to statistical analysis.

End-to-end AI-assisted meta-analysis pipeline orchestration from TOPIC.txt to final manuscript and reviewer responses. Use when the user provides a topic and wants the full meta-analysis workflow, tracking, and final paper.

Collect and manage full-text PDFs for included studies, track provenance, and prepare documents for extraction. Use when moving from screening to data extraction.

Draft and render a meta-analysis manuscript with Quarto using an IMRaD structure and embedded figures/tables. Use when preparing the final paper from analysis outputs.

Run statistical meta-analysis in R with renv, generate effect estimates, heterogeneity, and publication bias diagnostics, and export figures and tables. Use when analyzing extracted study data.

Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias. Use when selecting eligible studies for meta-analysis.

Conduct literature searches for meta-analysis using Python with uv, query PubMed and other databases, deduplicate results, and store round-based bibliographies with notes. Use when building or updating the evidence corpus.

Intake a meta-analysis topic from TOPIC.txt, translate it into a PICO or PECO protocol, and define eligibility, outcomes, and search scope. Use when starting a new meta-analysis project or refining a research question.