Reference and Traceability for Clinical Trial Pre-screening

Data Characteristics in This Domain

Clinical trial pre-screening data in medical affairs originates from global clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), internal pharmaceutical company research, academic journal articles, and regulatory agency documents. Update frequencies vary: registry information often updates in real-time or weekly, while academic papers and regulatory documents follow their publication cycles. Document structures are diverse, including structured database records, semi-structured PDF reports, and unstructured text descriptions. Fields and units are highly specialized. For example, "inclusion/exclusion criteria" often contain complex medical terminology and numerical ranges (e.g., "hemoglobin > 10 g/dL"), and "adverse event incidence" appears as percentages or specific counts.

Constraints on "Reference and Traceability" from These Characteristics

Data source diversity requires the reference system to handle multiple data formats and unify their indexing. Varying update frequencies mean the knowledge base needs incremental update and version control capabilities to ensure reference timeliness. Complex document structures, especially semi-structured and unstructured text, challenge text segmentation and semantic understanding, requiring more refined preprocessing strategies to extract effective information. Specialized fields and units, such as medical indicators and drug dosages, demand that reference snippets accurately retain original values and units, avoiding ambiguity or errors during extraction and restatement. For example, references to "inclusion/exclusion criteria" must be precise down to numerical ranges and units to effectively determine patient eligibility during pre-screening.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	800–1200 characters	Clinical trial documents often contain long paragraphs of inclusion/exclusion criteria or results descriptions; context integrity is essential.
`Recall count`	Top 10	Ensures coverage of multiple potentially relevant clinical trials or research reports, improving recall rate.
`Similarity threshold`	0.75–0.85	Balances recall and precision, avoiding irrelevant retrievals while not missing critical information.
`Rerank result count`	5	Further filters to the few most relevant references for pre-screening conditions, reducing LLM processing load.
`maxContext`	4096 tokens	Considering the complexity and density of specialized medical terminology, a larger context window is necessary for understanding.
`parseTimeoutSecs`	600 seconds	Handles large PDF clinical trial reports or research literature, ensuring sufficient parsing time.

Common Pitfalls

LLM-returned pre-screening results do not align with knowledge base references. This occurs when Similarity threshold is set too high, causing relevant but not perfectly matching documents to be missed.
In API call workflows, the knowledge base ID is not passed correctly, leading to reference function failure, indicated by missing reference sources in LLM responses.
After a knowledge base update, the LLM still references old data. This happens due to improper incremental synchronization configuration or refreshInterval not adjusted to actual update frequency.

How to Verify Configuration

Select a batch of clinical cases with clear inclusion/exclusion criteria. Submit them to the system for pre-screening. Check if the returned results accurately cite the relevant clinical trial's inclusion/exclusion criteria.
For each pre-screening conclusion returned by the LLM, verify that the reference source links are clickable and accurately point to the corresponding paragraph in the original document.
Simulate adding or modifying clinical trial data. Observe if the knowledge base's update mechanism completes synchronization within the configured refreshInterval and if subsequent queries can reference the updated data.
Through system logs or the monitoring panel, check if the knowledge base query's Recall count and Rerank result count align with configuration expectations. Also, monitor document parsing success rate under the parseTimeoutSecs parameter.

The values provided are common starting points. Measure performance against your own 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.