Data Characteristics in This Domain
CRO (Contract Research Organization) pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE) data, case report forms (CRF), adverse event (AE) reports, and post-market safety databases. This data exists in both structured (e.g., database records, XML files) and unstructured (e.g., free-text descriptions, medical imaging reports) formats. Data updates frequently, especially during clinical trials and early post-market phases, with adverse event reports potentially recorded daily or in real-time. Document structures are complex, containing medical terminology, dosage information, patient characteristics, adverse event codes (e.g., MedDRA), and event timelines. Fields and units are highly specialized, such as dosage units (mg, μg/kg), time units (days, hours), and event severity grades (CTC AE grading).
Constraints Imposed by These Characteristics on Citation and Traceability
The multi-source nature and high update frequency of CRO pharmacovigilance data require citation and traceability mechanisms to handle heterogeneous data effectively and maintain timeliness. Complex document structures and specialized terminology make traditional keyword-based retrieval difficult to achieve precise matches. This necessitates advanced semantic understanding to ensure citation relevance and accuracy. The presence of extensive free-text descriptions challenges information extraction and knowledge graph construction, affecting the completeness of the traceability chain. Strict regulatory compliance requirements, such as ICH-GCP and GVP, mandate that every citation must be traceable to the original data point to support audits and regulatory reviews. The system must identify cited content and locate its specific position in the original document, including specific paragraphs or fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Accommodates medical text paragraph length, balancing contextual completeness with retrieval efficiency. |
Recall count (Recall Count) | 8–12 items | Considers the relevance of adverse event reports, ensuring coverage of potentially related information while controlling inference costs. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Addresses the precision requirements of medical terminology, avoiding interference from low-relevance content but not filtering out synonymous or near-synonymous expressions. |
Rerank result count (Reranked Return Count) | 3–5 items | Focuses on the most core and relevant citations, improving the accuracy of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient file parsing time when processing large clinical trial reports or complex adverse event databases. |
MAX_KNOWLEDGE_BASE_SIZE_MB | 2000 MB | Accommodates the continuous growth of data in CRO projects, ensuring adequate knowledge base storage capacity. |
Three Common Mistakes
- Limited knowledge base citations, with only one citation provided per answer: The default
Recall count(Recall Count) setting is too low, failing to fully utilize multiple relevant documents in the knowledge base. - Key information missing or incorrect after parsing free-text descriptions: The
Chunk size(Segment Length) is too short, truncating critical medical descriptions or event sequences and affecting semantic understanding and information extraction. - Cited content inconsistent with original report fields or untraceable: Knowledge base construction lacks field-level or paragraph-level tagging of original data sources, preventing precise traceability.
How to Confirm Proper Configuration
- Select typical adverse event queries. Verify that the knowledge points cited in the answers comprehensively cover the relevant original report content.
- For specific adverse events, confirm that cited sources can be precisely traced back to specific paragraphs or fields in the original clinical trial report or AE database.
- Simulate queries of varying complexity. Observe the number of documents cited in the model's answers and compare it with the expected recall range.
- Check responses to newly entered adverse events. Confirm the system can promptly cite the latest data. Evaluate the timeliness of data updates and knowledge base synchronization.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.