Show Your Work: Verbatim Evidence Requirements and Automated Assessment of Large Language Models for Biomedical Text Processing of Trial Eligibility Criteria.

Introduction Large language models (LLMs) are used for biomedical text processing, but decisions are often hard to audit. We evaluated whether enforcing a mechanically checkable quote affects performance for trial eligibility-scope classification from abstracts. Methods We used 200 randomized controlled trials and provided models with the title and abstract. Trials were labeled with whether they allowed for the inclusion of patients with localized and/or metastatic disease. Flagship models from three vendors (OpenAI, Google, and Anthropic) were queried in two conditions: Label-only and label plus a verbatim supporting quote. Models could abstain if they deemed the abstract to not contain sufficient information. Each condition was repeated three times per abstract. Quotes were mechanically validated as exact substrings, and a separate judge step used an LLM to rate whether each quote supported the assigned label. Results Evidence requirements modestly reduced coverage, i.e., non-invalid non-abstained outputs (GPT-5.2 86.2% to 84.3%, Gemini 3 flash preview 98.3% to 92.8%, Claude Opus 4.5 96.0% to 94.5%) by increasing abstentions and, for Gemini, invalid outputs. Macro-F1 remained high but changed by model (slight gains for GPT-5.2 and Gemini, decrease for Claude). Labels were stable across repetitions (Fleiss' kappa 0.829 to 0.969). Mechanically valid quotes occurred in 83.3% to 91.2% of runs, yet only 48.0% to 78.8% of evidence-bearing predictions were judged semantically supported. Restricting to supported predictions increased macro-F1 at the cost of lower coverage. Conclusion Substring-verifiable quotes provide an automated audit trail and enable selective, higher-trust automation when applying LLMs to biomedical text processing. However, this approach introduces new failure modes and trades coverage for verifiability in a model-dependent way.