Deployment and Upgrade for CRO Clinical Trial Pre-screening

Data Characteristics in This Category

Data generated during the clinical trial pre-screening phase by Contract Research Organizations (CROs) primarily comes from sponsor-provided clinical study protocols, subject inclusion/exclusion criteria, prior research data, internal medical literature databases, and disease epidemiology data. This data has a relatively low update frequency, typically updating with project initiation or protocol amendments. Document structures are mainly unstructured text, such as investigator brochures, informed consent forms, case report form (CRF) templates, SOP documents, and structured database records. Fields and units are highly specialized, involving medical terminology, dosage units (e.g., mg/kg), time units (e.g., days, weeks), biological indicators (e.g., mmol/L), and often include multilingual descriptions.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The data characteristics of CRO clinical trial pre-screening introduce specific requirements for FastGPT deployment and upgrades. The large volume of specialized unstructured documents means that RAG (Retrieval Augmented Generation) system index construction requires robust text parsing capabilities and domain vocabulary recognition. Lower update frequency reduces the pressure for real-time index updates, but initial full data import and index construction during deployment can be time-consuming. Specialized fields and units demand that model training and fine-tuning focus on understanding medical terminology and entity recognition to prevent pre-screening result deviations due to unit confusion. Multilingual data requires the system to support multilingual processing to ensure accuracy in global clinical trials. Additionally, high demands for data security and compliance influence the choice of private deployment architecture and data transmission encryption.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial documents, such as investigator brochures, can be large single files.
`maxContext`	`8000 tokens`	Ensures the model can process longer subject inclusion/exclusion criteria and medical background descriptions.
`Chunk size`	`800 characters`	Balances the completeness of medical text context with retrieval efficiency.
`Rerank result count`	`Top 5 entries`	Improves relevance, ensuring the most important retrieval results are prioritized by the model.
`Similarity threshold`	`0.75`	Clinical pre-screening demands high recall accuracy; a lower threshold might introduce irrelevant information.
`reranker`	Calibrate based on actual measurements	Ensures accurate semantic relevance ranking for medical professional terminology.

Three Common Pitfalls

Reranker calls consistently return "false": This usually indicates a configuration issue with the Reranker service itself, such as an incorrectly set RERANKER_API_KEY or an unreachable service address.
Custom retrieval quantity cannot be increased to the desired value: The MAX_SEARCH_RESULTS parameter might not be correctly configured in the environment variables, leading the system to limit the maximum number of returned items per retrieval.
"Unsaved" prompt appears on the plugin editing interface after an upgrade: This could be related to front-end caching or the FASTGPT_VERSION environment variable not being updated to the latest version number, causing the browser to load outdated interface logic.

How to Confirm Correct Configuration

Upload a clinical study protocol PDF containing complex medical terminology. Check if its segmentation and indexing are complete and accurate, especially for critical inclusion/exclusion criteria.
Perform pre-screening queries using several typical subject profiles. Verify if the number of recalled results and the relevance ranking after re-ranking meet expectations, and compare them with manual judgment to determine the effectiveness of Similarity threshold and reranker.
Under the maxContext limit, input a long text of inclusion/exclusion criteria. Observe if the model's generated answer accurately references all key information in the text, thereby confirming its context processing capability.

The values provided 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.