Citation and Traceability for Mental Illness Clinical Trial

Data Characteristics in This Domain

Mental illness clinical trial data comes from diverse sources. These include clinical trial registries (e.g., ClinicalTrials.gov), medical journal databases (e.g., PubMed, Embase), pharmaceutical company internal research reports, and regulatory submission documents. Data update frequencies vary; registry information might update weekly, while journal articles depend on publication cycles. Document structures often mix unstructured text (e.g., trial protocols, research reports) with semi-structured data (e.g., JSON or XML summaries of trial results). Beyond general demographics, drug dosages, and efficacy metrics, fields include mental scale scores (e.g., HAM-D, PANSS), diagnostic criteria (e.g., DSM-5, ICD-10), and patient symptom descriptions. Units cover common dosage units (mg), time units (weeks), and specific scoring values for mental scales.

Constraints Imposed by These Characteristics on Citation and Traceability

The heterogeneity and varied update frequencies of mental illness data sources require a citation and traceability mechanism that integrates information from different sources effectively and marks its timeliness. Unstructured text content, especially patient symptom descriptions and trial protocols, involves complex and specialized language. This demands high accuracy in information extraction and matching. The presence of semi-structured data necessitates accurate parsing of structured fields alongside semantic understanding of unstructured parts when extracting key information. For unique fields like mental scale scores and diagnostic criteria, citations must ensure complete and accurate context to prevent misinterpretation from fragmented references. Additionally, data involving patient privacy requires strict adherence to ethical guidelines for citation and display, ensuring de-identification and noting access restrictions for original data sources during traceability.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Mental illness text descriptions are complex; an appropriate length helps preserve context and reduces semantic fragmentation.
`Recall count` (Recall Count)	`8–12 items`	Ensures coverage of multi-source information, improves recall for complex queries, and balances with response speed.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances recall and precision, avoids irrelevant information interference, and requires high accuracy for specialized terminology matching.
`Rerank result count` (Reranked Return Count)	`top 5 items`	Focuses on the most relevant results, reduces user reading burden, and improves traceability efficiency.
`Max Citation Depth`	`3 layers`	Most clinical trial data hierarchies are not excessively deep; limiting depth controls computational resources.
`ENABLE_SOURCE_MARKING`	`true`	Ensures each response segment can be clearly linked to its original document or data source.

Three Common Mistakes

A response fails to cite the exact question-answer pair from the knowledge base, instead providing an AI-rewritten version. This occurs when the Similarity threshold (Similarity Threshold) is set too low, causing the system to recall semantically similar but not original text segments.
In a workflow, after the AI queries the database, the response does not cite the query results. This typically happens if ENABLE_SOURCE_MARKING is set to false, or if metadata from the database query results is not correctly passed to the citation module.
System lag or slow response times when handling multi-layered user selection scenarios. This might be due to Recall count (Recall Count) or Chunk size (Chunk Size) being set too high, leading to unexpected computational load for vector retrieval and text processing.

How to Confirm Correct Configuration

Conduct multi-turn dialogue tests. Check if the source links cited in each response point to the correct original documents or data records.
Randomly select 10 complex queries. Verify if the cited sources fully and accurately support the key information in the response, and assess their relevance to specialized fields like mental scales and diagnostic criteria.
Simulate user input of common mental illness symptom descriptions. Observe if the system's returned citation segments include relevant clinical trial patient enrollment criteria or efficacy assessment indicators.
Test queries from different data sources (e.g., ClinicalTrials.gov records, PubMed paper abstracts). Ensure the system consistently provides citation information, and that the timeliness of citations aligns with original data updates.

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.