Data Characteristics for This Category
Attenuated inactivated vaccine clinical trial data primarily originates from public databases of global clinical research institutions and national drug regulatory agencies (e.g., ClinicalTrials.gov, European Medicines Agency database, China Clinical Trial Registry) and clinical research reports published in academic journals. Data update frequencies vary. Sponsor-registered trial information in databases may update in real time, but detailed results often publish months to years after trial completion. Document formats are diverse, including trial protocols (PDF), investigator brochures (PDF), case report form (CRF) templates (PDF/Word), statistical analysis plans (SAP) (PDF), study reports (PDF), and some structured data files (CSV/Excel) such as subject baseline characteristics, adverse events (AE), serious adverse events (SAE), laboratory test results. Fields include subject ID, study center, vaccine batch, dosage, immunization schedule, adverse reaction type and grade (e.g., CTCAE grade), serological test indicators (e.g., neutralizing antibody titers, cellular immune response intensity) and their units (e.g., IU/mL, log2), and safety follow-up duration.
Constraints on "Citing Sources and Traceability" from These Characteristics
The multi-source and heterogeneous nature of attenuated inactivated vaccine clinical trial data requires robust multi-format document parsing capabilities for citing sources and traceability. Non-structured documents like PDFs and Word files are prevalent. These may contain tables and charts. Extracting key information and accurately linking it to the original text presents a challenge. Data update latency means a Retrieval-Augmented Generation (RAG) system must clearly state the information's publication date or data version when citing to avoid using outdated information. Clinical trial reports contain specialized terminology, abbreviations, and strict dosage and unit representations. This requires the knowledge base to maintain semantic integrity during chunking. Avoid over-chunking, which can lead to context loss and affect traceability accuracy. Additionally, citation sources may scatter across multiple websites. Link validity and persistence are important considerations. The system needs to support dynamic link generation or provide stable citation paths.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Vaccine clinical trial documents are dense, containing complex medical terminology and experimental data. Longer chunks help maintain contextual integrity. |
Chunk Overlap Length (Chunk Overlap) | 100–150 characters | Ensures sufficient overlap between adjacent chunks to capture semantic connections across chunks, especially when describing adverse events or immunogenicity results. |
Recall count (Recall Count) | Top 5 | Clinical prescreening demands high accuracy. Appropriately increasing the recall count covers more potentially relevant information, reducing the risk of omissions. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Vaccine trial data has high semantic similarity. This threshold range effectively filters irrelevant content while retaining highly relevant clinical details. |
Rerank result count (Rerank Count) | Top 3 | Reranking based on recall further improves the alignment between the answer and the core question, focusing on key safety and efficacy data. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Clinical trial report files are often large, requiring more parsing time. Increasing the timeout prevents parsing interruptions. |
Three Common Mistakes
- The AI answer provides an inaccessible internal address instead of a direct link to the original document. This occurs when the knowledge base fails to correctly parse or store the external URL of the original document during upload.
- The system confuses antibody titer data from different batches and dosages when processing vaccine immunogenicity data, leading to inaccurate source citations. This happens when the knowledge base fails to effectively identify and differentiate key fields within tables during chunking.
- When retrieving adverse event incidence rates, the AI answer provides a higher value than the actual one. Investigation reveals the search results include a withdrawn early study report. The knowledge base fails to manage document versions effectively.
How to Confirm Correct Configuration
- Select a clinical trial report for an attenuated inactivated vaccine that includes complex tables and charts. Ask a question about a specific adverse event incidence rate. Verify if the source link cited in the AI answer directly points to the corresponding table or paragraph in the report.
- Ask questions about immunogenicity data for different vaccine batches or dosing regimens. Check if the AI answer accurately distinguishes and cites the relevant data sources, and verify the consistency of data units.
- Simulate a query containing outdated or revised information. Verify if the AI answer prioritizes citing the latest version of clinical trial data and confirms the document's publication date or version number through the cited source.
The values provided are common starting points. Measure them against specific 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.