Data Characteristics for This Category
Cardiovascular intervention medical device regulatory submission documents primarily include product technical requirements, test reports, clinical evaluation reports, risk management reports, instructions for use, and labels. Data sources typically come from internal R&D, manufacturing, and quality departments, as well as third-party testing institutions and clinical trial organizations. The update frequency of these documents varies. Product technical requirements and instructions for use might update with product iterations, while clinical evaluation reports are completed at specific stages. Document structures are highly standardized, following templates from the National Medical Products Administration (NMPA) or international standards (e.g., ISO 13485, MDR). Fields involve device model, specifications, material composition, performance parameters, test methods, clinical indications, and adverse events. Units strictly adhere to the International System of Units (SI) and industry conventions; for example, dimensions are in mm, pressure in kPa, and flow rate in mL/min.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The strong standardization of cardiovascular intervention device submission documents requires workflows to precisely match predefined fields during information extraction. Ambiguity or inference is unacceptable. For instance, extracting performance parameters from test reports must identify specific fields like nominal diameter and rated pressure and their corresponding values. Varying update frequencies mean that workflows must select appropriate knowledge base versions or retrieval strategies based on document type to ensure information timeliness. Highly standardized document structures enable structured information extraction through predefined templates and regular expressions, reducing reliance on unstructured text understanding. Additionally, strict unit requirements necessitate built-in unit conversion or validation mechanisms in data integration and comparison steps to prevent errors caused by unit inconsistencies.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4096 | Ensures most standard paragraph texts can be processed in a single pass |
Chunk size (Segment Length) | 500 characters | Accommodates common paragraph lengths in submission documents while maintaining semantic completeness |
Recall count (Recall Count) | Top 8 entries | Covers highly relevant knowledge points, reducing omissions |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall accuracy and avoids interference from irrelevant information |
Rerank result count (Rerank Return Count) | Top 3 entries | Filters out the most core and relevant knowledge snippets for output |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses potentially long parsing times for large PDF files |
Three Common Mistakes
- Symptom: The workflow frequently misses or incorrectly identifies device models during extraction. Reason: Insufficient regular expressions or pattern matching rules are configured for different manufacturers' model naming conventions.
- Symptom: When generating draft instructions for use, some performance parameters have units inconsistent with the product technical requirements. Reason: The workflow lacks a step for unit standardization or validation of data from different document sources.
- Symptom: The workflow does not provide options or branches for users to decide whether to skip knowledge base retrieval. Reason: Workflow design did not incorporate user interaction decision points into the process, leading to insufficient flexibility.
How to Confirm Proper Configuration
- Select a complete set of submission documents, run the workflow, and compare the output structured data with the original documents to verify the accuracy of key field extraction.
- Randomly select multiple test reports containing different units. Verify whether the workflow correctly handles unit conversion or provides warnings for unit inconsistencies during data integration and comparison.
- Set different knowledge base retrieval strategies in the workflow and manually simulate scenarios where users choose to skip retrieval. Observe if the workflow behaves as expected.
- Check the workflow's log output to confirm if clear error codes or warning messages are present when handling abnormal data or unrecognized document structures.
The values provided are common starting points and should be measured against specific 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.