Data Characteristics
IVD diagnostic reagent clinical trial data originates from clinical study reports, Electronic Medical/Health Records (EMR/EHR) systems, Laboratory Information Management Systems (LIMS), and patient follow-up records. This data typically combines structured and unstructured formats. Structured data includes patient demographics, diagnostic results, test indicators (e.g., sensitivity, specificity, accuracy), reagent batch information, and adverse event records. This data commonly resides in databases or spreadsheets. Unstructured data often appears in researcher notes, progress notes, and text descriptions within medical imaging reports. Data update frequency varies across clinical trial phases, ranging from daily updates (e.g., patient vital signs) to periodic updates (e.g., interim analysis reports). Document structures are rigorous, adhering to regulatory requirements like GCP (Good Clinical Practice). Fields and units are highly standardized; for example, test indicators often include units (e.g., ng/mL, IU/L), and date formats are uniform (e.g., YYYY-MM-DD).
Constraints Imposed by Data Characteristics on Multi-Turn Conversations and Prompts
The highly structured and standardized nature of IVD diagnostic reagent data demands accuracy in multi-turn conversations and precision in prompts. First, a large volume of structured data means the system must accurately identify and extract specific field information during pre-screening, such as patient inclusion/exclusion criteria or specific biomarker levels. This requires prompts to clearly instruct the model on which key entities and values to focus on, and to perform logical judgments. Second, medical terminology and specialized descriptions in unstructured text necessitate strong semantic understanding from the model to avoid pre-screening biases caused by misinterpreting professional vocabulary. Third, variations in data update frequency affect the real-time nature of the knowledge base and the timeliness of conversation results. The model must identify the recency of information in multi-turn conversations and adjust answer prioritization accordingly. Finally, strict compliance requirements mean the conversation system must provide results and trace their sources. Prompt design must consider how to guide the model to cite original document snippets to support decision transparency and credibility.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 1200 token | Ensures complete coverage of a typical query's context and key details |
temperature | 0.2 | Reduces model divergence, improves result accuracy and controllability |
Recall Count | 8 | Balances recall breadth with model processing load, ensuring no critical information is missed |
Similarity Threshold | 0.75 | Increases the relevance of recalled results, reducing interference from irrelevant information |
Reranked Return Count | 3 | Selects the most relevant document snippets, optimizing model input quality |
System Prompt | Includes "As a clinical trial pre-screening assistant, strictly judge based on provided patient data and reagent standards, and provide the basis for judgment." | Defines the model's role and task boundaries, emphasizing evidence and transparency |
Three Common Mistakes
- The conversation result outputs no judgment or basis, only repeating the user's question. This occurs because the prompt does not clearly guide the model to make judgments and cite source documents.
- The system frequently asks for already provided information in multi-turn conversations. This occurs because
maxContextis set too low, preventing the model from effectively remembering previous conversation content. - A
CORSerror is encountered when the frontend requests the/api/v1/chat/completionsinterface. This typically happens when the frontend and backend are deployed on different domains, and the backend is not configured with the appropriate Cross-Origin Resource Sharing policy.
How to Confirm Proper Configuration
- For a series of typical pre-screening questions, observe whether the model can accurately extract patient information, compare reagent standards, and provide clear judgment results.
- Check if the model can cite original document snippets from the knowledge base in its answers, verifying the accuracy of the cited sources.
- Simulate multi-turn conversations to assess whether the model can effectively maintain contextual coherence and avoid repetitive questioning.
- Test queries of varying complexity to observe if system response times are stable and if
httprequests are triggered as expected.
Note: The values provided are common starting points. Measure them against specific samples to determine the optimal configuration for a particular use case.
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.