Data Characteristics in This Category
Infectious disease clinical trial pre-screening data primarily originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), specialized medical journals, reports from disease control and prevention agencies, and public research data released by pharmaceutical companies. This data updates frequently. Data for new or epidemic diseases may update weekly or even daily, while chronic infectious disease data typically updates monthly or quarterly. Document structures are mainly structured tabular data and unstructured text reports. Structured data includes trial design, inclusion/exclusion criteria, investigational drugs, research centers, subject numbers, primary and secondary endpoints. Field names often contain disease name abbreviations, dose units (e.g., mg/kg, IU), and time units (e.g., days, weeks). Unstructured text includes research protocols, ethics approvals, and informed consent forms.
Constraints Imposed by These Characteristics on Citation and Traceability
High-frequency data sources require the system to quickly capture and process new or modified content, ensuring pre-screening results are based on the latest information. The coexistence of structured and unstructured data necessitates hybrid retrieval capabilities, allowing for precise matching of structured fields and understanding of medical terminology and context within unstructured text. The large volume of medical terminology and abbreviations requires accurate identification and linking to original definitions or sources during citation and traceability to avoid ambiguity. Consistency in units, especially for dosage and time, is crucial to prevent information distortion due to unit conversion errors. Furthermore, emergency clinical trials during epidemics have rapid data release speeds and diverse formats, demanding higher real-time performance and robustness for citations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 1500 characters | Infectious disease research reports often contain lengthy methodological descriptions and data tables. |
Recall Count | Top 8 | Needs to cover data from different sources and types to ensure comprehensive recall. |
Similarity Threshold | 0.78 | Precisely matches medical terminology and key information, reducing false positives. |
Rerank Return Count | Top 3 | Prioritizes displaying the most relevant and authoritative citation sources. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large research reports and attachments, preventing parsing timeouts. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Allows uploading attachment reports containing multimedia or high-resolution images. |
Three Common Mistakes
- Citation links in pre-screening results are broken or point to incorrect pages. This occurs because the original data source's URL structure changed or the document was deleted.
- The system fails to extract key inclusion/exclusion criteria fields from complex PDF research reports. This happens because the PDF parser's ability to recognize tables and multi-column layouts is insufficient, leading to empty fields.
- Dose units for the same drug are inconsistent when processing different data sources, leading to confusion between
mgandμgduring citation traceability. This is due to a lack of a unified unit standardization process.
How to Confirm Proper Configuration
- Randomly select at least 20 infectious disease clinical trial reports from different sources. Verify that key information (e.g., primary endpoints, inclusion/exclusion criteria) from each report can be accurately cited and traced back to the corresponding location in the original document.
- Simulate a clinical trial pre-screening scenario for a recently emerging infectious disease. Check if the system's returned citation sources include the latest relevant research and if their update timestamps meet expectations.
- For unstructured reports containing tables and figures, check if the system can correctly identify and cite data points within tables and text descriptions next to figures. Ensure the
maxContextsetting covers this content. - Use queries containing specific medical abbreviations or technical terms. Verify that the system's returned citation sources accurately explain these terms and provide their context within the original document.
The values given 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.