Data Characteristics
Data for Contract Development and Manufacturing Organizations (CDMOs) in clinical trial pre-screening primarily originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), regulatory agency databases (e.g., FDA, EMA), scientific literature, patent information, and internal research and development reports. Data updates frequently, especially for clinical trial progress and regulatory policy changes, typically on a weekly or monthly basis. Document structures vary, including structured data (e.g., trial protocols, patient enrollment criteria, drug dosages, adverse event reports) and unstructured text (e.g., investigator brochures, meeting minutes, expert opinions). Fields and units are highly specialized, such as trial phase (Phase I/II/III), disease classification (ICD-10 codes), biomarker concentrations (ng/mL, nM), gene mutation types, and active pharmaceutical ingredient (API) content (mg/g). The data often contains extensive medical terminology, abbreviations, and jargon that require precise identification and parsing.
Constraints Imposed by These Characteristics on "Reference and Traceability"
The diversity and specialized nature of CDMO clinical trial pre-screening data demand high standards for reference and traceability. Structured data requires accurate field mapping to prevent misinterpretations due to inconsistent units or coding differences. Extracting key information from unstructured text requires the AI Agent to accurately identify medical entities and relationships, avoiding incorrect citations or omission of critical context. High update frequency means continuous synchronization of knowledge base content is necessary; referencing outdated data can lead to erroneous decisions. The prevalence of specialized terms and abbreviations requires the Agent to accurately cite original sources and provide term explanations when generating responses, enhancing credibility. Furthermore, regulatory compliance mandates that all references must be traceable to their original sources, ensuring data transparency and reliability to prevent potential legal risks.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000 characters | Accommodates the contextual needs of long texts like clinical trial protocols while managing computational resource consumption. |
Recall count (Recall Count) | 15 items | Ensures coverage of multiple relevant data sources, improving the comprehensiveness of information recall. |
Similarity threshold (Similarity Threshold) | 0.78 | Requires a higher similarity match in medical texts to reduce interference from irrelevant information. |
Rerank result count (Rerank Return Count) | 5 items | Prioritizes the most relevant core information for the query, improving response efficiency. |
Citation Content Template (Reference Content Template) | "{source_title}: {chunk_content}" | Clearly displays the source title and specific quoted content, allowing users to quickly locate information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles complex file parsing for large research reports or merged multiple documents, preventing timeouts. |
Common Pitfalls
- The AI response does not provide specific reference source links, preventing users from verifying information accuracy and origin. This occurs if the
Citation Content Template(Reference Content Template) does not include thesource_urlvariable or if the knowledge base fails to correctly extract URL fields. - Cited data in the response does not align with the latest regulatory requirements, leading to inaccurate pre-screening results. This typically happens due to an inadequate knowledge base update mechanism that fails to synchronize with the latest regulatory database information in a timely manner.
- For queries regarding critical parameters like patient enrollment criteria, the AI returns overly broad reference content, failing to precisely point to specific values or ranges. This may be due to an excessively large
Chunk size(Chunk Length) setting, causing individual knowledge blocks to contain too much irrelevant information.
How to Confirm Correct Configuration
- Submit a query about a specific drug's clinical trial phase. Check if the response includes the corresponding trial phase information and provides a clickable link to the original registration website.
- Enter a query containing specialized medical terminology. Verify that the AI response correctly explains the terms and cites their definitions from the investigator brochure.
- Simulate a query about a newly published regulatory policy. Observe if the AI can cite the latest policy text and compare it with older policies.
- Check the indexing of adverse event reports in the knowledge base. Ensure that each adverse event entry can be traced back to its original report file.
The values provided 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.