Data Characteristics
Respiratory disease clinical trial pre-screening data originates from global clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), medical journal articles, conference abstracts, pharmaceutical company announcements, and de-identified patient recruitment information from Electronic Health Records (EHRs). This data updates frequently; trial status and recruitment details can change weekly or even daily. Document structures vary, including structured trial protocol summaries, unstructured investigator brochures, medical report PDFs, and web-based patient recruitment criteria descriptions. Fields cover disease diagnoses (e.g., ICD-10 codes), inclusion/exclusion criteria text, drug dosages, treatment durations, adverse event reports, and biomarker test results. Units for dosage are typically milligrams (mg) or micrograms (μg), duration is in days or weeks, and biomarkers use their respective international standard units.
Constraints on Knowledge Base Retrieval and Recall
The high update frequency of respiratory clinical trial data requires the knowledge base to support efficient incremental updates and version management. This ensures timely retrieval results. Diverse document structures, especially a large volume of unstructured text, make pure keyword matching ineffective. Stronger semantic understanding is necessary to capture subtle differences in inclusion/exclusion criteria. Synonyms, near-synonyms, and variations in phrasing can impact pre-screening accuracy. Inconsistent standardization of fields and units requires the retrieval model to recognize and process the same concepts expressed differently, for example, "asthma" and "bronchial asthma." Complex patient recruitment logic, such as "age between 18-65 years and FEV1 between 50%–80% predicted," demands support for multi-condition combined retrieval and numerical range matching to avoid missing eligible potential subjects.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Clinical trial documents have long paragraphs with complex logic. Shorter segments lose context; longer segments introduce noise. |
Recall count (Recall Count) | 15–20 items | Ensures coverage of enough potentially relevant documents, balancing recall rate with subsequent processing efficiency. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Respiratory disease descriptions contain many synonyms and subtle differences. This range balances relevance with semantic flexibility. |
Rerank result count (Rerank Return Count) | Top 5 items | After semantic reranking, the top 5 items typically contain the most critical information highly matching pre-screening conditions. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF clinical trial protocols or investigator brochures requires longer parsing times. |
ENABLE_AUTO_UPDATE | Enabled | Clinical trial data updates frequently. Enabling auto-update keeps knowledge base content current. |
Common Pitfalls
- Retrieval results contain many irrelevant or outdated clinical trial details. The knowledge base lacks a regular update mechanism, leading to stale data.
- Inability to precisely filter subjects based on specific age ranges or biomarker values. The model fails to understand numerical ranges or unit differences, causing matching failures.
- Knowledge base responses do not provide specific paragraph citations from the source. Users cannot trace information origin. This occurs if paragraph citation is not enabled in knowledge base versions below
4.9.7, or if the configuration is not correctly applied.
Verification Steps
- Perform simulated pre-screening queries using typical patient profiles for specific respiratory diseases (e.g., COPD, asthma). Check if recall results include known highly relevant trials.
- Select recently updated clinical trial registration information. Verify if the knowledge base has synchronized the latest status, for example, a trial status changing from "recruiting" to "completed."
- Input queries containing numerical ranges (e.g., "FEV1 > 80%") and specific medical terminology. Check if retrieval results accurately identify and match corresponding inclusion/exclusion criteria.
- Review knowledge base responses. Confirm that each response paragraph includes a corresponding source citation link or document identifier at the end.
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.