boster-hub/llm-surgical-free-text-annotation: Version 2.0.0 – Reproducible Python pipeline

Abstract

Overview Version 2.0.0 provides the revised reproducible Python pipeline for binary symptom annotation in German emergency department reports using locally deployed open-weight large language models. This release supersedes the original R-based workflow published in version 1.0.0. The earlier code and prompts remain available in the legacy/ directory for historical transparency. Included in this release PDF text extraction using Poppler pdftotext symptom-specific stratified development and validation splitting local LLM inference through Ollama deterministic response parsing and explicit tracking of invalid responses temperature analysis across nine candidate models symptom-specific model screening and selection negation-aware rule-based baseline patient-level bootstrap confidence intervals paired comparison of selected LLMs and the rule-based baseline preparation of discrepancies for manual consensus review exact German zero-shot prompts for all four symptoms machine-readable manifest containing the exact Ollama model digests pinned Python dependencies and detailed execution documentation Reproducibility The analysis uses fixed random seeds, predefined parsing rules, explicit inference settings, and exact content-addressed Ollama model identifiers. Model metadata are provided in model_manifest.csv. Clinical source reports, extracted clinical text, patient identifiers, reference annotations, raw model outputs, and manual review files are not included because of data-protection requirements. Compatibility This is a major revision. The current Python workflow replaces the R-based workflow from version 1.0.0 and should be used for the revised manuscript analysis.