Data Characteristics
Clinical trial data for rehabilitation devices primarily originates from hospitals, rehabilitation centers, and research institutions. Data updates typically align with trial cycles and interim report submissions, ranging from weekly to quarterly. Document types are diverse. They include trial protocols, informed consent forms, case report forms (CRFs), ethics committee approval letters, subject screening logs, adverse event reports, device instruction manuals, calibration reports, and follow-up records. These documents often come in PDF, DOCX, and XLSX formats. Fields and units are highly specialized. Examples include device serial numbers, calibration dates, treatment durations (e.g., 30 minutes), treatment intensities (e.g., 50 Hz), patient functional assessment scale scores (e.g., FIM total score 90), adverse event codes (e.g., AE.001), and specific device performance parameters (e.g., torque 5 Nm, pressure 20 kPa).
Constraints from "Document Parsing and Chunking"
The structured nature of rehabilitation device clinical trial documents varies significantly. Some PDFs may be scanned images. This means OCR recognition quality directly impacts subsequent parsing. Case report forms and screening logs often contain extensive tabular data. Accurate identification of row and column relationships is necessary. Device instruction manuals and calibration reports have high densities of technical parameters. Chunking requires semantic completeness to avoid separating critical parameters from their descriptions. Subject screening logs contain patient privacy information, such as names and ID numbers. The parsing process must effectively identify and handle this information to ensure data compliance. Document update frequency is not high, but each update may involve extensive revisions. Incremental updates and version management capabilities are necessary to avoid redundant processing and data confusion. The presence of complex units and specialized terminology requires high domain adaptability from tokenization and vectorization models.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Balances semantic completeness and context length, preventing critical information from being truncated. |
Overlap Size | 50–100 characters | Ensures contextual continuity at chunk boundaries, improving recall. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the parsing time required for large PDFs and complex tables. |
min_text_length | 30 characters | Filters out short text snippets that are OCR errors or meaningless. |
OCR_ENABLED | true | Processes a large number of scanned PDFs, ensuring text extraction. |
TABLE_EXTRACTION_ENABLED | true | Accurately parses tabular data in case report forms and screening logs. |
Common Pitfalls
- Uploaded PDF documents have missing content or garbled text because OCR was not enabled or configured, preventing text recognition from scanned images.
- Key technical parameters in clinical trial protocols have incomplete semantics during recall because long documents were chunked too short, separating parameter descriptions from values.
- Patient information in screening logs was not effectively identified or anonymized because the parser failed to recognize privacy fields in specific formats.
Verification Steps
- Select different types of rehabilitation device clinical trial documents (e.g., scanned PDFs, DOCX files with tables). Upload them and check the text quality and semantic completeness of the chunks in the knowledge base.
- Verify that key fields extracted from informed consent forms and case report forms (e.g., subject ID, device model, adverse event description) are accurate, without obvious garbling or truncation.
- Simulate queries for specific device parameters or trial stages. Check that the recalled chunks contain complete contextual information, without critical information loss.
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.