Data Characteristics for This Category
Registration dossiers for respiratory system diseases involve diverse data sources. These include clinical trial reports, pharmacological and toxicological study data, manufacturing process documents, quality standards, and non-clinical study reports. Data update frequencies vary. Clinical trial data may update quarterly during a trial, while manufacturing processes or quality standards typically update only when changes occur. The primary document structure follows ICH M4E's Common Technical Document (CTD) framework, subdivided into administrative information and CTD Modules 1 through 5. Fields and units are highly specialized. Examples include lung function indicators like FEV1 (Forced Expiratory Volume in 1 second, unit L) and FVC (Forced Vital Capacity, unit L), drug concentration units of µg/mL or ng/mL, and adverse event coding often using MedDRA terminology. Some data exist as charts, graphs, or imaging reports (e.g., chest X-rays, CT scans), requiring specific parsing capabilities.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The complex data characteristics of respiratory system dossiers impose several constraints on tool calling and plugins. First, diverse data sources and varying update frequencies require tools with flexible data ingestion and version management capabilities. This ensures that called data are current and complete. Second, the multi-module document structure under the CTD framework necessitates precise targeting of specific modules and sections during tool calls, avoiding interference from irrelevant information. For example, in the pharmacovigilance module, accurate extraction of MedDRA-coded adverse event reports is critical. Specialized fields and units, such as FEV1 and FVC, require tools to correctly identify and process these specific measurement units during data extraction and comparison, preventing misinterpretation or unit conversion errors. The presence of charts, graphs, and imaging reports demands that plugins integrate image recognition or OCR capabilities to structure key information and link it with textual data. Lacking these capabilities can lead to inaccurate data extraction, information omissions, and potentially impact dossier compliance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 16000 | Accommodates large clinical reports and research overviews common in respiratory system dossiers. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDF files, such as clinical study reports, preventing parsing timeouts. |
Chunk size | 800–1200 characters | Balances the completeness of clinical data and pharmacological/toxicological descriptions with model processing efficiency. |
Recall count | Top 8 entries | Ensures coverage for multi-dimensional information retrieval needs within the dossier. |
Similarity threshold | 0.78 | Precisely matches specific terminology and report content related to respiratory system diseases, reducing noise. |
Rerank result count | Top 3 entries | Re-ranks complex medical terminology and data to improve relevance. |
Common Pitfalls
- Symptom: Lung function data returned after a tool call shows inconsistent units, sometimes L, sometimes mL. Reason: The plugin failed to correctly identify and standardize unit expressions in the raw data, or did not perform unit conversions.
- Symptom: When calling the MCP service, the system displays "cannot connect" or "service timeout." Reason: The target service interface address is misconfigured, or the MCP service itself is inaccessible due to network policies or authentication issues.
- Symptom: The large language model's output contains citation markers like "[1]," affecting readability. Reason: The tool did not remove or format citation markers before final output when processing knowledge base retrieval content.
How to Verify Configuration
- Perform multiple queries for core lung function indicators (e.g., FEV1, FVC) using different units. Verify the consistency of units in the returned results.
- Simulate a complete MCP service call. Check interface logs to confirm the service responded successfully and the returned data structure meets expectations.
- Query knowledge base content containing numerous citation markers. Observe if the model's output removes unnecessary citation markers or formats them acceptably.
The values provided are common starting points. Measure them 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.