| name | KSB-D10-K0023 |
| description | Predictive Evidence Modeling: Future evidence forecasting, study outcome prediction, evidence evolution modeling, research priorit... |
| version | 1.0.0 |
| domain | D10 |
| domain_name | Benefit-Risk Assessment |
| type | Knowledge |
| proficiency_level | L1 |
| bloom_level | understand |
| triggers | ["explain predictive evidence modeling","what is predictive evidence modeling","explain predictive evidence modeling"] |
| epa_mapping | EPA-03, EPA-05, EPA-08 |
| cpa_mapping | CPA-02, CPA-03 |
| regulatory_refs | CIOMS-X, ICH-E2E |
KSB-D10-K0023: Predictive Evidence Modeling
Overview
Domain: D10 - Benefit-Risk Assessment
Type: Knowledge
Proficiency Level: L1 (Novice - Direct supervision required)
Bloom Level: Understand
Description
Future evidence forecasting, study outcome prediction, evidence evolution modeling, research prioritization algorithms
Context
- Major Section: Benefit-Risk Assessment Theoretical Foundation and Decision Science
- Section: AI-Enhanced Evidence Processing and Synthesis
EPA Mapping
- EPA-03:3006-3008
- EPA-05:3011-3012
- EPA-08:3018-3019
CPA Pathway
Regulatory References
Instructions
When this skill is activated, Claude should:
- Demonstrate L1 proficiency in predictive evidence modeling
- Apply understand level cognitive skills to explain the topic
- Reference relevant regulatory guidance (CIOMS-X, ICH-E2E)
- Connect to related EPAs: EPA-03, EPA-05, EPA-08
Key Competencies
- Future evidence forecasting, study outcome prediction, evidence evolution modeling, research prioritization algorithms
Assessment Criteria
- Can explain core concepts independently
- Demonstrates understanding of regulatory context
- Applies knowledge appropriately to PV scenarios
Related Skills
- Other D10 skills in AI-Enhanced Evidence Processing and Synthesis
- Cross-domain integrations per DAG architecture
Generated from PV KSB Framework v1.0 | 2025-12-31
