Data Characteristics for this Category
ADC clinical trial pre-screening data primarily originates from clinical trial registries (e.g., ClinicalTrials.gov, EudraCT from the European Medicines Agency), internal pharmaceutical company R&D databases, academic journals, conference abstracts, and regulatory approval documents. This data updates frequently, with new trial registrations, changes in patient recruitment status, and result publications occurring often. Document structures vary, including structured trial protocol summaries, unstructured Investigator's Brochures (IB), Informed Consent Forms (ICF), and various reports. Fields and units are highly specialized. For example, "Target" typically refers to specific protein names, "Conjugation Method" includes linker types and conjugation sites, and "Dosage" involves mg/kg or μg/kg, often accompanied by dosing frequency and cycle.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The high update frequency of ADC clinical trial data requires the knowledge base to have an efficient incremental update mechanism to ensure retrieval result timeliness. Diverse document structures mean the knowledge base must support multimodal document parsing and accurately extract key information from different formats. The precision of specialized fields demands high-quality tokenization and entity recognition capabilities. For instance, accurately identifying ADC drug names, targets, and linker types is crucial to avoid recall bias due to synonyms, abbreviations, or inconsistent naming conventions. Retrieving numerical fields like dosage requires support for range queries and unit conversion to meet the needs of clinical pre-screening for patients within specific dosage windows. Furthermore, trial data involves extensive medical terminology and complex drug mechanism descriptions. Traditional keyword matching offers limited recall effectiveness. Semantic understanding capabilities are needed to effectively capture query intent.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters (characters) | ADC trial documents contain lengthy background descriptions and method details. Moderately extending chunk size helps maintain contextual completeness and prevents key information from being truncated. |
Overlap Size | 100–150 characters (characters) | Ensuring sufficient contextual overlap between adjacent chunks helps capture related information that spans chunk boundaries, especially when describing drug mechanisms. |
Recall count (Recall Count) | Top 10–15 entries (top 10–15 items) | Clinical trial pre-screening requires comprehensive consideration of multiple factors. Increasing the recall count provides richer candidate information, reducing the risk of missing critical trials. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Given the specialized and diverse nature of ADC trial descriptions, setting a relatively high similarity threshold helps filter out irrelevant trial information, focusing on the best matches. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5 items) | After initial recall, a reranking model further optimizes relevance, ultimately presenting the most accurate few results to the user, improving pre-screening efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | When processing large Investigator's Brochures or complex trial protocols, file parsing can be time-consuming. Appropriately extending the timeout prevents failures due to incomplete parsing. |
Three Common Mistakes
- Uploading files with Chinese names results in garbled characters. This occurs when the system's default encoding does not match the filename encoding, leading to errors in file path or metadata parsing.
- Retrieval results contain a large amount of irrelevant or outdated ADC trial information. This happens when the knowledge base fails to synchronize with the latest data in a timely manner, or indexing strategies do not effectively handle changes in trial status.
- Queries for specific dosages fail to recall relevant results, showing fewer than expected or no returns. This may be because the tokenizer incorrectly identifies dosage units or lacks support for numerical range queries.
How to Confirm Proper Configuration
- Upload various ADC trial documents (e.g., IB and trial protocols in PDF, DOCX formats). Check if the file parsing status in the knowledge base displays "successful" (success) and if the text content is previewable.
- Execute queries for specific ADC drugs, targets, and key indicators. Verify if the recalled results include known important clinical trials and if key fields (e.g., dosage, dosing frequency) in the results are accurate.
- Simulate historical queries and compare the consistency of query results with previous versions. Simultaneously, verify if newly registered ADC clinical trials can be retrieved promptly to assess the knowledge base's update effectiveness.
The values provided are common starting points. Measure them against your own samples to determine the optimal configuration.
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.