Data Characteristics for This Category
Data for academic promotion clinical trial pre-screening primarily originates from global clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), medical journal databases (e.g., PubMed, Medline), official pharmaceutical company research reports, and industry conference materials. This data updates frequently; some registries update daily, while journal data updates with publication cycles. Document structures typically include both structured fields and unstructured text. Structured fields encompass trial ID, study name, disease area, interventions, primary/secondary endpoints, inclusion/exclusion criteria, research institutions, and study status. Unstructured text covers trial protocol summaries, detailed descriptions, and research outcome interpretations. Special fields include dosage units (mg/kg, IU), time units (weeks, months, years), and disease staging standards (TNM staging, ECOG score).
Constraints Imposed by These Characteristics on "Reference and Traceability"
High-frequency data sources demand an efficient incremental update mechanism for the knowledge base to ensure the timeliness of referenced information. The coexistence of structured and unstructured data requires the knowledge base to handle both precise field matching and semantic understanding during information extraction. For example, the accuracy of dosage units and disease staging standards within inclusion/exclusion criteria directly impacts pre-screening results. This necessitates that references point to specific values or standard definitions. The multi-source, heterogeneous nature increases the complexity of data cleaning and integration. Tracing references requires clear indication of the source registry or the specific section of a document. User queries often involve English literature, so the knowledge base needs to support cross-language knowledge retrieval and referencing to address issues where English literature cannot be cited.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Balances semantic completeness with retrieval efficiency, preventing long paragraphs from diluting key information. |
Recall count (Number of Retrieved Items) | Top 5–8 items | Clinical trial pre-screening demands high accuracy; increasing the number of retrieved items enhances relevance coverage. |
Similarity threshold (Similarity Threshold) | Calibrate by measurement | Tune based on specific datasets and model performance using small-batch annotated data to ensure retrieved results are both relevant and precise. |
Rerank result count (Number of Reranked Items) | Top 3 items | After optimization by the reranking model, the top few results typically possess the highest precision and relevance, reducing information redundancy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large clinical trial protocols or annual reports can be time-consuming; this provides sufficient time to avoid timeouts. |
maxContext | 4000 tokens | Ensures enough context information can be accommodated, especially for complex inclusion/exclusion criteria and trial descriptions. |
Three Common Pitfalls
- The knowledge base fails to cite English literature. This occurs even when relevant English literature exists in the knowledge base, but responses only cite Chinese materials or fail to cite anything. This is due to an incomplete multilingual knowledge base index or a retrieval model not optimized for cross-language matching.
- Inaccurate numerical values for inclusion/exclusion criteria are cited in pre-screening results, such as incorrect or missing dosage units. This happens when the unit information for specific fields is not correctly identified or standardized during data import, leading to incomplete data storage in the knowledge base.
- After a user query, cited source links in the response are invalid or point to irrelevant pages. This occurs when the knowledge base does not validate source link validity during data updates, or when links are not precisely associated with specific content segments during storage.
How to Confirm Proper Configuration
- Select several typical clinical trial pre-screening questions. Check if the cited source links in the responses are valid and accurately point to the corresponding sections in the original literature or database.
- Randomly sample queries containing specific units like dosage, time, or disease staging. Verify if the numerical values and units cited in the response exactly match the original data.
- For queries containing mixed Chinese and English content, validate if the knowledge base can cite both Chinese and English literature, and manually assess the relevance of the citations.
Note: The values provided are common starting points and should be measured against specific 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.