Citation and Traceability for mRNA Vaccine Clinical Trial

Data Characteristics

mRNA vaccine clinical trial pre-screening data primarily comes from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), pharmaceutical company internal R&D databases, academic journals, conference abstracts, and public documents from regulatory agencies (e.g., FDA, EMA). This data updates frequently, often weekly or monthly, with new trial registrations, result releases, and protocol amendments. Document structures vary, including structured trial protocol summaries, unstructured Investigator's Brochures, informed consent forms, and research papers in PDF format containing figures and statistical results. Key fields include NCT ID (Clinical Trial Identifier), Trial Status, Study Design, Intervention Type, Primary Outcome Measures, Eligibility Criteria, and Adverse Events. Units typically used are ug or mg for dosage, days, weeks, months, years for time periods, and persons for patient counts.

Constraints on Citation and Traceability

The diverse and frequently updated nature of mRNA vaccine clinical trial data demands high accuracy and timeliness for citation sources. Unstructured documents, such as PDF Investigator's Brochures, pose challenges for information extraction and traceability, requiring more refined text segmentation and embedding strategies. Rapidly updating data sources necessitate frequent knowledge base synchronization to ensure cited information is current, preventing decisions based on outdated data. Integrating multi-source data, such as structured metadata from ClinicalTrials.gov and detailed results from journal articles, requires the system to differentiate the weight and reliability of various sources. Furthermore, specific fields like Eligibility Criteria often contain complex logic and medical terminology, challenging semantic understanding and precise matching, which affects the granularity of traceability. Failure to accurately identify and cite this key information can lead to biases in pre-screening results or an inability to provide sufficient evidence.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`500 characters`	Ensures sufficient contextual information while preventing excessively long segments from impacting recall quality.
`Overlap Length`	`50 characters`	Maintains semantic continuity between paragraphs, preventing key information from being split.
`Recall count` (Recall Count)	`Top 8 entries`	Given the complexity of clinical trial documents, increasing recall covers more potentially relevant information.
`Similarity threshold` (Similarity Threshold)	`0.78`	Balances recall rate and accuracy, filtering out irrelevant or weakly related segments.
`Rerank result count` (Rerank Return Count)	`Top 3 entries`	Prioritizes the most relevant citations, improving user efficiency in obtaining core information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates parsing time for large Investigator's Brochures and research paper PDFs, preventing timeouts.

Common Pitfalls

Query results include citations for questions not present in the knowledge base. This can occur if the similarity threshold is set too low, causing irrelevant document segments to be recalled.
The conversation request interface does not return cite citation IDs. This typically happens if the citation ID return function is not enabled in the knowledge base configuration or if the model output format does not include this field.
Variable references in knowledge base search cards are not effective. This may be due to a mismatch between variable names and knowledge base fields, or incorrect citation syntax.

Verification Steps

For typical queries, check if the returned citation sources accurately point to specific sections or paragraphs in the original documents. Compare the original document content with the cited text for consistency.
Simulate updates with newly published trial data. Verify that the knowledge base synchronization mechanism can timely capture and index the latest information. Test queries to confirm they can cite this new data.
Use queries containing complex medical terminology and logic from eligibility criteria. Check if the system can precisely identify and cite key information within the Eligibility Criteria field. Evaluate the completeness of the cited content.

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.