Data Characteristics
Data sources for Contract Research Organizations (CROs) in clinical trial pre-screening are diverse. They include clinical trial protocols, patient recruitment criteria, historical study data, medical literature, and regulatory guidelines from drug agencies. Data update frequencies vary. Protocols and recruitment criteria are relatively stable after a trial starts but may update locally due to protocol amendments. Medical literature and regulatory guidelines are continuously published.
Document structures vary. Protocols are typically structured PDF or Word documents, containing detailed inclusion/exclusion criteria, study design, and drug information. Medical literature often consists of journal articles with abstracts, introductions, methods, results, and discussions. Historical study data may be in tables or database records.
Fields and units involve patient demographics (e.g., age years, weight kg), disease diagnoses (ICD-10 codes), laboratory test results (e.g., blood count g/L, liver/kidney function umol/L), imaging report descriptions, and medication history. Data types are complex, containing many specialized terms and abbreviations.
Constraints from Data Characteristics on Source Citation and Traceability
The complexity of CRO clinical trial pre-screening data sources demands robust source citation and traceability capabilities. The mix of structured and unstructured text requires strong document parsing and information extraction. The prevalence of specialized terms and abbreviations necessitates precise semantic understanding in the retrieval system to avoid inaccurate or missing citations due to terminology differences.
Varying update frequencies mean the knowledge base must support incremental updates and version management to ensure citation timeliness. Especially for critical information like patient inclusion/exclusion criteria, any citation error can impact trial compliance and safety. Therefore, the system must precisely point to specific paragraphs or data points in original documents and provide verifiable paths. This meets CRO requirements for data accuracy and traceability, supporting audits and compliance reviews.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Clinical protocols and medical literature often have long paragraphs. This length ensures contextual completeness while preventing single segments from becoming too large, which could impact retrieval efficiency. |
Recall count | 10–15 entries | This ensures coverage of multiple relevant data sources and different matching perspectives for complex queries. |
Similarity threshold | 0.75–0.85 | Clinical trial pre-screening demands high accuracy. A high threshold effectively filters out irrelevant or ambiguous citations. |
Rerank result count | 5 entries | This reduces the number of citations presented to the engineer, increasing information density and focusing on the most relevant key information. |
maxContext | 3000 Tokens | This ensures the large language model has sufficient context when processing complex inclusion/exclusion criteria or multi-factor comprehensive judgments. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | CRO documents, especially PDFs, are often large. This provides ample time for parsing, preventing timeout failures. |
Common Pitfalls
- Irrelevant citations in conversations: The knowledge base's
Similarity threshold(similarity threshold) is set too low, failing to effectively filter out low-relevance text blocks. - Missing or incomplete content in cited sources: The document's
Chunk size(segment length) is too short, causing key information to be truncated or context to be lost. - Failure to retrieve the latest clinical guidelines or protocol revisions: The knowledge base has not undergone timely incremental updates, leading to citations of outdated information.
How to Verify Configuration
- Perform multiple queries for several typical complex inclusion/exclusion criteria. Verify that citation sources accurately point to corresponding descriptions in the original documents.
- Randomly select a percentage of citations. Verify that their content,
fields,units, andvaluesare exactly consistent with the original documents. Check for parsing errors. - Simulate scenarios of clinical protocol revisions or new literature releases. Update the knowledge base, then query relevant content to confirm that citations reflect the latest information.
The values provided are common starting points. They should be measured 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.