Data Characteristics
Phase II-III clinical trial pre-screening data comes from Clinical Study Protocols, Investigator's Brochures (IB), Case Report Forms (CRF), and related medical literature, disease guidelines, and drug inserts. These documents typically exist as PDFs, Word files, or structured databases. Data updates are infrequent, mainly occurring during protocol amendments, adverse event reports, and interim data summaries. Documents have complex structures, containing extensive specialized terminology, abbreviations, charts, and tables. Key fields include inclusion/exclusion criteria, subject demographics, disease diagnosis, concomitant medications, co-morbidities, laboratory test results, imaging results, and Adverse Event (AE) and Serious Adverse Event (SAE) records. Units are typically international standard units or common clinical units, such as mg/dL, mmol/L, mmHg, and IU/L.
Constraints on Knowledge Base Retrieval and Recall
The complexity of Phase II-III clinical trial pre-screening data poses multiple challenges for knowledge base retrieval and recall. The prevalence of specialized terminology and abbreviations requires tokenizers and embedding models with strong domain understanding. This avoids recall failures due to semantic ambiguity or imprecise vocabulary matching. Complex document structures, including charts and tables, mean that simple text segmentation may miss critical information. This necessitates considering multimodal or structured information extraction and indexing. Although data updates are infrequent, each update may involve core inclusion/exclusion criteria adjustments. This means the knowledge base needs to support efficient version management and incremental updates, ensuring the timeliness and accuracy of recall results. Furthermore, the high precision required for inclusion/exclusion criteria means recall results must be highly accurate, not just relevant. This prevents mis-screening or missed screenings, directly impacting subsequent clinical decisions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters (characters) | Paragraphs in clinical protocols have strong semantic integrity. Shorter segments may lose context, while longer ones introduce noise. |
Recall count (Recall Count) | Top 8-12 entries (top 8-12 entries) | Phase II-III clinical inclusion/exclusion criteria often involve multiple dimensions. This ensures coverage of enough relevant information snippets. |
Similarity threshold (Similarity Threshold) | 0.78-0.85 | Ensures recall results are highly relevant to the query, reducing inaccurate specialized terminology matches. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5 entries) | Combined with large language model reranking, this focuses on the most critical and accurate inclusion/exclusion criteria or patient characteristics. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles parsing large PDF documents, especially protocol files containing complex tables and charts. |
embedding_model | text-embedding-ada-002 or text-embedding-3-large | Selects embedding models with strong generality and good performance in the medical domain, or models fine-tuned for the domain. |
Common Pitfalls
- Knowledge base query results contain a large amount of irrelevant or generalized information. This happens because the segmentation strategy is too coarse, failing to adequately consider the specialized and structural nature of clinical documents, leading to semantic mixing within individual segments.
- API calls return a 429 status code. This occurs when the request volume exceeds the rate limit of the model or interface within a short period, especially during file uploads and batch indexing.
- After multi-turn conversations, the accuracy of answers to the same question decreases. This is due to improper context management, where the model fails to effectively integrate historical conversation information for precise recall in subsequent turns.
How to Verify Configuration
- For core inclusion/exclusion criteria, construct diverse queries. Check if recall results contain all necessary information snippets and verify their accuracy.
- Randomly select key information points from clinical trial protocols. Query the knowledge base and cross-reference the model's answers with the original document content for consistency.
- Simulate actual pre-screening scenarios. Input patient data with boundary conditions. Observe the inclusion/exclusion criteria recalled by the knowledge base. Determine if it can effectively differentiate eligible from ineligible subjects. Adjust the similarity threshold based on expert domain opinion.
The values provided are common starting points. Measure them against your 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.