Data Characteristics for This Category
Market access clinical trial pre-screening data primarily originates from global clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), regulatory agency databases (e.g., FDA Orange Book, EMA Human Medicines), medical literature (e.g., PubMed, Embase), and industry reports. This data updates frequently. Clinical trial registration information may update weekly, and literature data publishes continuously. Document structures vary. They include structured clinical trial protocols, unstructured research report PDFs, drug labels, and semi-structured market access assessment reports. Key fields include disease indications, drug mechanisms of action, trial phases, primary/secondary endpoints, subject inclusion criteria, biomarkers, competitor information, market size forecasts, and pricing strategies. Common units for dosage are milligrams (mg) and micrograms (μg). Efficacy metrics often use percentages (%) and mean values.
Constraints Imposed by These Features on Multi-turn Conversation and Prompts
The breadth of data sources and update frequency require the model to process large amounts of heterogeneous information and maintain knowledge base timeliness. Unstructured documents make information extraction and structuring critical pre-processing steps, impacting the accuracy of subsequent multi-turn conversations. Specialized terminology and abbreviations in clinical trial protocols and market access reports necessitate prompt design that standardizes terms and disambiguates context. Complex logic in subject inclusion criteria and biomarkers requires multi-turn conversations to guide user input precisely, capturing accurate screening conditions. The dynamic nature of competitor information and market size forecasts requires prompts to adapt flexibly to information changes and avoid generating outdated or inaccurate suggestions.
Configuration Settings
| Configuration Item | Suggested Value | Basis for This Value |
|---|---|---|
maxContext | 6 | Ensures dialogue history covers complex clinical trial pre-screening logic |
prompt | Specific template | Presets roles and tasks, guiding users to provide key screening information |
system_prompt | Version v1.2 | Defines model behavior boundaries, handles professional terms and sensitive information |
temperature | 0.2–0.4 | Reduces content generation randomness, improves screening result accuracy |
Recall count | Top 10 entries | Balances recall efficiency with relevance, covering potential matches |
Similarity threshold | Calibrate by actual measurement | Precisely controls knowledge recall accuracy based on domain data characteristics |
Three Common Mistakes
- Key screening criteria are missing in the conversation, leading to inaccurate results. This occurs when prompts fail to effectively guide users to provide complete inclusion or exclusion criteria.
- The model cites outdated or incorrect market data. This happens when the knowledge base is not updated promptly, or prompts do not emphasize citing the latest information.
- The system returns a "no relevant information found" message, even when relevant content exists in the knowledge base. This indicates the
Similarity thresholdis set too high, making recall overly strict and failing to match semantically similar entries.
How to Confirm Proper Configuration
- Conduct end-to-end testing. Input typical clinical trial pre-screening scenarios and verify that output results include all necessary information.
- Evaluate the model's ability to understand specialized terminology and abbreviations in multi-turn conversations, ensuring correct context disambiguation.
- Check whether the model can cite the latest market access data and provide reasonable suggestions after knowledge base updates.
- Randomly sample multi-turn conversation records. Manually assess whether the conversation flow is smooth and user intent is accurately captured.
The values provided are common starting points. Measure them against specific samples to determine optimal settings.
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.