Official Verified

ebm-calculator

Evidence-Based Medicine calculator for sensitivity, specificity, PPV, NPV, NNT, and likelihood ratios. Essential for clinical decision making and biostatistics education.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/aipoch-ai/ebm-calculator

Download Source Code (.zip)

EBM Calculator

Evidence-Based Medicine diagnostic test calculator.

Features

Sensitivity / Specificity calculation
PPV / NPV with prevalence adjustment
Likelihood ratios (LR+ / LR-)
Number Needed to Treat (NNT)
Pre/post-test probability conversion

Parameters

Parameter	Type	Default	Required	Description
`--mode`, `-m`	string	diagnostic	No	Calculation mode (diagnostic, nnt, probability)
`--tp`, `--true-pos`	int	-	*	True positives (diagnostic mode)
`--fn`, `--false-neg`	int	-	*	False negatives (diagnostic mode)
`--tn`, `--true-neg`	int	-	*	True negatives (diagnostic mode)
`--fp`, `--false-pos`	int	-	*	False positives (diagnostic mode)
`--prevalence`, `-p`	float	-	No	Disease prevalence 0-1 (diagnostic mode)
`--control-rate`	float	-	**	Control event rate 0-1 (nnt mode)
`--experimental-rate`	float	-	**	Experimental event rate 0-1 (nnt mode)
`--pretest`	float	-	***	Pre-test probability 0-1 (probability mode)
`--lr`	float	-	***	Likelihood ratio (probability mode)
`--output`, `-o`	string	stdout	No	Output file path

* Required for diagnostic mode
** Required for nnt mode
*** Required for probability mode

Output Format

{
  "sensitivity": "float",
  "specificity": "float",
  "ppv": "float",
  "npv": "float",
  "lr_positive": "float",
  "lr_negative": "float",
  "interpretation": "string"
}

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

No hardcoded credentials or API keys
No unauthorized file system access (../)
Output does not expose sensitive information
Prompt injection protections in place
Input file paths validated (no ../ traversal)
Output directory restricted to workspace
Script execution in sandboxed environment
Error messages sanitized (no stack traces exposed)
Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

Successfully executes main functionality
Output meets quality standards
Handles edge cases gracefully
Performance is acceptable

Test Cases

Basic Functionality: Standard input → Expected output
Edge Case: Invalid input → Graceful error handling
Performance: Large dataset → Acceptable processing time

Lifecycle Status

Read Full Documentation on GitHub

Metadata

Author@aipoch-ai

Stars4473

Updated2026-05-01

View Author Profile

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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-aipoch-ai-ebm-calculator": {
      "enabled": true,
      "auto_update": true
    }
  }
}

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