Model Integration and Configuration for Clinical Trial

Data Characteristics

Clinical trial pre-screening data in regulatory affairs primarily originates from publicly available trial protocols, investigator brochures, recruitment information, and results reports. These are published by major global clinical trial registries such as ClinicalTrials.gov, EudraCT (EU Clinical Trials Register), and ChiCTR (Chinese Clinical Trial Register). Data typically exists in a mixed format of structured (e.g., XML, JSON) and unstructured (e.g., PDF documents, Word documents) content. Update frequency is high, with some registries updating daily. Document structure is relatively fixed, including trial title, study objectives, study design, inclusion/exclusion criteria, interventions, and primary/secondary outcome measures. Field names and units generally follow international medical and pharmaceutical standards. Examples include dose units like mg, g; time units like days, weeks, months; disease diagnoses using ICD codes; and drug names following WHO ATC classification.

Constraints on Model Integration and Configuration

The diversity of clinical trial pre-screening data requires multimodal processing capabilities for model integration, especially for parsing and extracting information from unstructured PDF documents. High update frequency necessitates support for incremental indexing and rapid updates to ensure the timeliness of pre-screening results. The fixed document structure allows for the use of predefined field templates when configuring information extraction models, improving extraction accuracy. The use of medical terminology and international standard codes requires models to accurately understand domain-specific vocabulary during vectorization and retrieval, preventing semantic drift. For example, the same drug might appear with both a brand name and a generic name in different documents; the model must recognize their association. Additionally, due to the extensive medical terminology, the model's context window configuration must be large enough to accommodate complex medical descriptions.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters` (characters)	Ensures each chunk contains a complete medical concept or inclusion/exclusion criterion, preventing semantic truncation.
`Recall count` (Recall Count)	`Top 10–15 entries` (top 10–15 items)	Considers the complexity and relevance requirements of clinical trials, increasing recall to improve coverage.
`Similarity threshold` (Similarity Threshold)	Calibrated by empirical measurement	Balances recall and precision, avoiding retrieval of too many irrelevant results or missing critical information.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (top 5 items)	Focuses on the few most relevant items, improving engineer reading efficiency.
`Model Context Window`	`32k tokens`	Accommodates complex descriptions and long sentence structures in medical documents, preventing information loss.
`File Parsing Timeout`	`600 seconds` (seconds)	Provides sufficient parsing time for large PDF documents or documents containing complex tables.

Common Pitfalls

The model returns too few or too many screening results, indicated by an abnormal results_count field. This occurs when Recall count (Recall Count) and Similarity threshold (Similarity Threshold) are not finely tuned to the data characteristics.
Some specialized medical terms or drug names are not correctly identified by the model, leading to relevant trials not being recalled. This happens if the chosen model has not been sufficiently pre-trained on medical domain data, or if the vectorization model lacks adequate understanding of domain-specific vocabulary.
When integrating Tongyi multimodal models, a 404 body not found error occurs. This may be due to incorrect mapping of the Tongyi model's API path or request body format in the proxyAI or one-api proxy services.

Verification Steps

Select a batch of typical clinical trial documents with clear inclusion/exclusion criteria and manually verify if the trials recalled by the model meet expectations.
For specialized terms or entities not correctly identified by the model, examine the embedding results of the vectorization model to assess the accuracy of their semantic representation.
Monitor API call status codes in the model logs to ensure stable connectivity with third-party model services, for example, 200 OK.
Compare the model's pre-screening results with human screening results, analyze discrepancies, and adjust Similarity threshold (Similarity Threshold) and Rerank result count (Rerank Return Count) based on these differences.

The values provided are common starting points and should be measured against your 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.