Clinical-Trial-Inspector

🥈Silver

The Clinical Trial Inspector is a cutting-edge AI tool that empowers researchers and clinicians to derive actionable insights from clinical trial data. Utilizing advanced techniques like Semantic Search and Visual Analytics, it enhances data exploration and decision-making.

600Updated 1w ago

Advanced30 minutes to implementresearch

Saves ~210 min per use

Quick InstallView Source

claude install geoffkip/Clinical-Trial-Inspector

Works with:

ChatGPTGitHub Copilot

Overview

About This Skill

Use Cases

Extracting insights from clinical trial data

Visualizing trial results for presentations

Conducting semantic searches for specific trial criteria

Generating reports on clinical trial trends

Best For

ResearchGrowth

Setup & Installation

Quick Install

Terminal

claude install geoffkip/Clinical-Trial-Inspector

Alternative Install (Git Clone)

git clone https://github.com/geoffkip/Clinical-Trial-Inspector

Requirements

Claude Code or compatible AI agent
Works with: ChatGPT, GitHub Copilot
Advanced skill - requires experience with AI agents

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze the clinical trial data for [DRUG_NAME] targeting [DISEASE]. Identify key trends in patient response, adverse events, and efficacy metrics. Highlight any outliers or anomalies that warrant further investigation. Suggest potential adjustments to the trial protocol based on these findings.

Example Output

After analyzing the clinical trial data for 'Nexarib' targeting 'Metastatic Breast Cancer', several key trends emerged. The primary efficacy endpoint, Progression-Free Survival (PFS), showed a median of 10.5 months in the treatment arm versus 7.8 months in the control arm. However, a notable outlier was observed in Cohort B, where 15% of patients experienced a severe immune-related adverse event (irAE) not seen in other cohorts. This could be linked to the concurrent use of corticosteroids, as 80% of these patients were on a medium to high dose. Additionally, the response rate in patients with PD-L1 expression ≥50% was significantly higher (45%) compared to those with lower expression (22%). Based on these findings, I recommend implementing a more rigorous monitoring protocol for irAEs in Cohort B and considering a stratified analysis based on PD-L1 expression levels in future reports.