Reference Source and Traceability for Bispecific Antibody

Data Characteristics for This Category

Bispecific antibody clinical trial data primarily originates from major global clinical trial registries (e.g., ClinicalTrials.gov, EudraCT, WHO ICTRP) and internal pharmaceutical company databases. Update frequency typically aligns with trial progress, with concentrated updates during key phases (e.g., recruitment initiation, primary results publication). Document structures are a mix of structured tables (e.g., CSV, XML formats for trial protocols, subject screening criteria) and unstructured text (e.g., trial reports, investigator brochures, scientific papers). Core fields include NCT ID, Trial Phase, Indication, Target, Drug Name, Inclusion/Exclusion Criteria, Dosage, Route of Administration, and Adverse Events. Common units appear for dosage (mg/kg, mg), time (days, weeks, months), and biomarker concentration (ng/mL, pM/L).

Constraints Imposed by These Characteristics on "Reference Source and Traceability"

The mixed structure of bispecific antibody clinical trial data demands accurate parsing of reference sources. Structured data allows direct field matching. However, extracting critical information from unstructured text relies on efficient entity recognition and relationship extraction, which directly impacts the granularity and recall of cited documents. The cyclical nature of data updates means the knowledge base requires regular synchronization to ensure timely references. The complexity and multi-condition combinations of key fields like Inclusion/Exclusion Criteria mean simple keyword matching is insufficient for precise traceability. Furthermore, varying data formats across different source platforms necessitate standardization during FastGPT's data ingestion phase; otherwise, this affects subsequent reference consistency. Sensitive information such as dosage and targets must have references that point to their original sources to meet compliance and verifiability requirements.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500-800 characters`	Balances semantic integrity of unstructured text with retrieval efficiency, preventing key information fragmentation during splitting.
`Recall count` (Recall Count)	`10-15 items`	Ensures coverage while reducing interference from irrelevant information and improving the efficiency of subsequent re-ranking.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Balances recall and precision, ensuring reference relevance, especially for matching complex `Inclusion/Exclusion Criteria`.
`Rerank result count` (Re-ranked Return Count)	`3-5 items`	Focuses on the most relevant references, allowing users to quickly locate core information and reduce cognitive load.
`maxContext`	`3000-4000 tokens`	Accommodates enough reference snippets to handle the complex descriptions found in bispecific antibody clinical trial protocols.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses the time required to parse large clinical trial reports (PDF, DOCX), preventing timeout interruptions.

Three Common Mistakes

The AI's answer fails to cite critical Dosage or Route of Administration information because knowledge base chunks are too short, separating these fields from their semantic context.
The knowledge base contains trial data for a specific NCT ID, but the AI's answer does not cite it. This might be due to inaccurate entity recognition in unstructured documents during data ingestion, leading to the NCT ID not being correctly indexed.
When a user asks about Adverse Events, the AI's answer shows citations, but the cited content is irrelevant to the user's question. This occurs when the Similarity Threshold is set too low, recalling a large amount of generalized content.

How to Confirm Proper Configuration

Submit representative queries for different Trial Phases and Indications, then verify if the NCT ID in the cited documents matches the original data source.
Select queries involving complex Inclusion/Exclusion Criteria and examine the specific text snippets cited by the AI's answer. Ensure they are complete and accurately traceable to the corresponding descriptions in the original document.
After simulating a data update, re-query for the latest clinical progress on a specific Target. Verify if the information cited by the AI's answer is the most recent version and points to the updated source.

Note: The values provided above are common starting points. Measure performance against your own samples and adjust as needed.

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.