Data Characteristics for this Category
Clinical trial pre-screening data for Contract Sales Organizations (CSOs) originates from sponsor-provided subject inclusion/exclusion criteria, trial protocols, historical case data, and internal patient recruitment experience. Data updates frequently, typically immediately after protocol revisions, inclusion/exclusion criteria adjustments, or patient recruitment progress reports. Document formats vary, including PDF trial protocols, Excel or CSV case data, and structured or semi-structured data exported from internal systems. Fields are highly specific, containing patient disease diagnosis codes (e.g., ICD-10), specific genetic test results, imaging assessment indicators (e.g., RECIST criteria), and detailed medication history. Units are complex, such as dosage units (mg/kg), time units (weeks, months), and biomarker concentration units (ng/mL).
Constraints Imposed by "HTTP Interface and External Systems"
The highly specific and diverse nature of CSO clinical trial pre-screening data requires HTTP interfaces to possess flexible data parsing capabilities. This adapts to varying data formats from different sponsors. PDF trial protocols necessitate key information extraction via OCR or document parsing services, increasing interface call complexity and reliance on external system stability. Frequent data updates demand interfaces support high-concurrency writes and real-time data synchronization. This ensures pre-screening results rely on the latest information. Complex fields and units, especially medical terminology, require strict format validation and semantic parsing during data transmission and verification. This prevents pre-screening deviations due to data errors. Patient sensitive information dictates data transmission must follow strict security protocols, such as HTTPS, and may require additional authentication and authorization mechanisms.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
HTTP_REQUEST_TIMEOUT_SECONDS | 60 seconds | PDF parsing and complex data validation may require extended processing time |
MAX_PAYLOAD_SIZE_MB | 20 MB | Accommodates request bodies with large amounts of structured data or encoded files |
RETRY_COUNT | 3 times | Addresses transient external service failures or network fluctuations, improving success rates |
BACKOFF_STRATEGY | Exponential Backoff | Prevents overwhelming external systems with excessive pressure in a short period |
AUTH_HEADER_NAME | Authorization | Follows industry standards, facilitating integration with various authentication mechanisms |
DATA_ENCODING | UTF-8 | Ensures correct transmission of medical terminology and special characters |
Common Misconfigurations
- Symptom: HTTP request returns a 400 status code with an "Invalid field format" error. Reason: External system receives data field formats that do not match expectations. Examples include numeric fields containing non-numeric characters or incorrect date formats.
- Symptom: Some critical medical indicators (e.g., gene mutation status) are empty or inaccurate in pre-screening results. Reason: The HTTP interface failed to correctly identify or extract specific medical terminology or data formats during PDF document parsing, leading to data loss.
- Symptom: A request to an external drug database interface times out. Reason: The request carries too many parameters, or the external database takes too long to process complex queries, exceeding the
HTTP_REQUEST_TIMEOUT_SECONDSlimit.
Verification of Configuration
- Use a test set containing complete and anomalous data for core subject inclusion/exclusion criteria. Call the HTTP interface to simulate pre-screening and compare returned results against expected outcomes.
- Check FastGPT internal logs. Ensure all HTTP request response status codes are 200 or other success indicators. Verify no connection timeouts or parsing failures occurred.
- Submit several representative clinical trial protocols via the HTTP interface. Verify that key medical fields (e.g.,
ICD_CODE,GENE_MUTATION) in the data returned by the external system are accurately identified and populated. - Simulate high-concurrency scenarios. Observe if data synchronization latency and error rates remain within acceptable limits under sustained high load. This evaluates the effectiveness of
RETRY_COUNTandBACKOFF_STRATEGY.
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.