Data Characteristics
Live attenuated vaccine clinical trial data originates from clinical research organizations, hospitals, and laboratories. This data exists in various modalities: structured tables (e.g., CRF forms), unstructured text (e.g., case reports, informed consent forms, ethics approvals, study protocols), imaging reports, and genetic sequencing results. Data update frequency is high during the trial period, stabilizing after follow-up completion. However, adverse event reports may continue to update. Document structures are complex. Case reports typically include fields such as patient demographics, medical history, medication history, physical examinations, laboratory tests, and adverse event records. Units involve dosage (mg, μg), concentration (IU/mL), time (days, weeks, months), and biomarker values. High precision is required, often with specific medical terminology and abbreviations.
Constraints on Multi-turn Conversations and Prompts
The multi-modal nature of live attenuated vaccine clinical trial data requires multi-turn conversation systems to integrate and understand different data sources. For example, the system must extract basic patient information from structured data and identify adverse event descriptions from unstructured text. High data update frequency challenges the real-time synchronization and indexing efficiency of the knowledge base, ensuring conversations are based on the latest information. Complex document structures and extensive medical terminology necessitate prompt designs that accurately recognize professional vocabulary and understand semantics. This avoids pre-screening result deviations due to ambiguous terminology. Precise numerical units mean the system must correctly parse and compare numerical values for dosage and time information. This directly impacts the logic for determining inclusion/exclusion criteria in multi-turn conversations.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 8 | Clinical trial pre-screening conversations often require tracing patient information and criteria judgments across multiple turns. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Vaccine clinical trial documents are content-dense; longer segments help maintain medical semantic integrity. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures coverage of multiple complex inclusion/exclusion criteria, improving relevant information recall. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Accurately matches clinical criteria, preventing misjudgments or omissions due to semantic ambiguity. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Selects the most relevant snippets from a high recall set to optimize context. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Vaccine clinical trial reports may contain large images and detailed text. |
Common Pitfalls
- A "Cannot read properties of null (reading 'q')" error in a conversation typically occurs when the backend service processes a query, and the passed parameter object lacks the required
qfield, leading to a null pointer exception. - Multi-turn conversations fail to correctly identify a patient's specific medication history, leading to inaccurate pre-screening results. This may be because the prompt did not adequately guide the model to focus on precise matching of drug names and dosages.
- When multiple files are uploaded, the system fails to associate different file contents for comprehensive judgment. This happens when the workflow configuration's file processing logic does not correctly implement parallel parsing and integration of multiple file inputs.
Verification Steps
- Conduct multi-turn conversation tests with simulated clinical cases. Ensure the system correctly identifies and extracts key patient information from case reports, such as age, disease history, and medication status.
- Upload clinical trial protocols containing various inclusion/exclusion criteria. Verify that the system can correctly determine if a patient meets all conditions based on multi-turn conversations.
- Check the knowledge base index status. Confirm that all the latest research progress and adverse event reports related to live attenuated vaccines are successfully synchronized and recallable.
- Compare the model's pre-screening results with expert human judgments. Evaluate the system's accuracy in understanding and comparing numerical fields like dosage and time. Adjust the
Similarity threshold(Similarity Threshold) if necessary.
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.