Implementation of a Locally Deployed Qwen2.5-7B-Instruct Pipeline for Structured Classification of Elbow MRI Reports.

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Abstract

Large language models (LLMs) are increasingly used for radiology report extraction, but many published applications lack the implementation details needed for reproducibility. This technical report provides a complete implementation-level description of a locally deployed LLM pipeline for structured classification of radiology reports. We describe a report classification system built around Qwen2.5-7B-Instruct, an open-weight instruction-tuned LLM served through a local Ollama runtime rather than a cloud application programming interface, and applied to a corpus of 10,222 elbow magnetic resonance imaging (MRI) reports. The report details the complete processing workflow, including report ingestion and column resolution, cohort-defining pattern matching, batch construction and threaded execution, the structured prompt used to generate two binary classification labels per report, output parsing, single-retry failure handling, and statistical routines used to summarize extracted data. To document the implementation comprehensively, we also describe the creation of an independently reviewed reference set of 400 reports, including reviewer blinding and file-separation procedures, as well as configuration factors that may influence reproducibility in locally deployed LLM workflows, including model quantization, sampling temperature, runtime version, and practical approaches for controlling these variables. This report is intended as a standalone technical reference that enables readers to understand, reproduce, evaluate, or adapt the described pipeline for related musculoskeletal or radiology report extraction tasks without requiring access to the original codebase.

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