Data Characteristics
Data for cardiovascular intervention clinical trial pre-screening originates from global clinical trial registries (e.g., ClinicalTrials.gov), medical literature databases (e.g., PubMed, Embase), and internal electronic health record (EHR) and clinical trial management systems (CTMS). Data updates frequently. New trial registrations, patient recruitment progress, and research results continuously emerge. Document structures vary. They include standardized trial protocols, informed consent forms (ICF), case report forms (CRF), and unstructured medical imaging reports and electrocardiogram (ECG) analysis reports. Key fields include disease diagnosis (e.g., ICD-10 codes), interventional device models, treatment plans, follow-up results, adverse event (AE) reports, and patient physiological indicators (e.g., heart rate, blood pressure, ejection fraction, typically in beats/min, mmHg, %).
Constraints on Citation and Traceability from Data Characteristics
The high update frequency of cardiovascular intervention clinical trial pre-screening data requires the knowledge base to quickly synchronize the latest information. This ensures timely and relevant citations. Diverse document structures necessitate robust parsing capabilities. Extract key information from unstructured text and link it with structured data. For example, identify intervention sites and device information from imaging reports, and extract inclusion/exclusion criteria from trial protocols. The accuracy of key fields directly impacts pre-screening results. Therefore, citations must precisely point to specific fields and units in the original data to avoid ambiguity. For instance, citing a patient's "ejection fraction 55%" requires traceability to the corresponding examination report in the EHR. If a citation only displays a large paragraph without locating the specific value, its traceability value significantly decreases.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 300–500 characters (characters) | In clinical trial documents, key information often concentrates in short sentences or paragraphs. Overly long chunks dilute information density, hindering precise citation. |
Recall count (Recall Count) | 8–12 entries (items) | Cardiovascular intervention pre-screening involves multiple dimensions. Sufficient contextual information is needed for comprehensive judgment. Increasing the recall count improves coverage. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | This ensures semantic relevance of recalled content and avoids introducing too much irrelevant information, which is critical for clinical judgment accuracy. |
Rerank result count (Rerank Return Count) | 5 entries (items) | After reranking, the top few most relevant citations often provide the core evidence needed for decision-making, reducing redundancy. |
maxContext | 3000 Tokens | Considering the complexity of clinical trial protocols and patient medical records, sufficient context is needed to understand and integrate information, preventing loss of critical details. |
Citation Merging | Merge by Source Document | This ensures citations from the same clinical trial protocol or patient medical record maintain integrity, facilitating traceability and contextual understanding. |
Three Common Mistakes
- Phenomenon: Model replies cite a large paragraph, but critical numerical values or judgment criteria within it are vague. Reason:
Chunk size(Chunk Length) is set too high. This causes a chunk to contain too much irrelevant information, making it difficult for the model to pinpoint specifics. - Phenomenon: Pre-screening results are inaccurate. The model fails to cite the latest clinical trial progress or device batch information. Reason: The knowledge base data synchronization mechanism is inadequate. It fails to timely update the latest data from ClinicalTrials.gov or device databases.
- Phenomenon: After merging and sorting results from different knowledge bases in a workflow, critical diagnostic criteria are overshadowed by secondary information. Reason:
Similarity threshold(Similarity Threshold) is set too low. This leads to the recall of a large amount of broadly related content, and the sorting logic fails to effectively differentiate priorities.
How to Confirm Proper Configuration
- Select typical pre-screening cases in cardiovascular intervention. Verify that the source documents cited in the model's replies precisely point to specific paragraphs or fields. For example, check if "patient ejection fraction 50%" can be traced back to the corresponding value in the original medical record.
- Examine citations in the knowledge base regarding new devices or the latest clinical guidelines. Confirm that their version numbers and publication dates align with official sources. Conduct sample checks at the preset update frequency.
- For a set of test questions with known correct answers, evaluate whether the items recalled by the model in the citation phase include all key information supporting the correct answer. Check if the similarity scores of the recalled items are above the set
Similarity threshold(Similarity Threshold).
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.