Precision filtering with chain-of-thought reasoning
High recall in earlier stages results in massive noise; manual filtering is infeasible at scale (108K papers).
Use LLMs with chain-of-thought reasoning and expert-informed prompts to classify papers by title/abstract only.
Does the abstract mention biological aging or related theories?
Are specific aging theories or mechanisms explicitly discussed?
Filter out clinical trials, reviews without theory focus, purely intervention papers
Handle ambiguous cases: hallmarks papers, senolytic studies, evolutionary theories
Assign confidence level (high/medium/low) based on theory centrality
Output structured JSON: include/exclude/review + reasoning
72.2% noise reduction while maintaining high recall for true aging theory papers
Papers discussing "hallmarks of aging" are included only if they propose or test specific mechanistic theories, not just review existing hallmarks.
Intervention papers are included if they explicitly test or discuss underlying aging theories (e.g., cellular senescence theory), excluded if purely pharmacological.
Papers on evolutionary theories of aging (antagonistic pleiotropy, disposable soma) are always included, even if abstract is brief.
Reviews are included if they synthesize or compare theories; excluded if they only summarize empirical findings without theoretical framing.