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name: grade-assessment description: Apply the GRADE framework to assess certainty of evidence in systematic reviews. Use when users need to rate evidence quality, create Summary of Findings tables, or understand the factors that affect confidence in effect estimates. license: Apache-2.0 compatibility: Works with any AI agent; GRADE methodology is universal metadata: author: meta-agent version: "1.0.0" category: evidence-assessment domain: evidence-synthesis difficulty: advanced estimated-time: "20 minutes" prerequisites: meta-analysis-fundamentals, heterogeneity-analysis

GRADE Assessment

This skill teaches the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework for assessing certainty of evidence.

Overview

GRADE is the internationally recognized standard for rating the quality of evidence in systematic reviews. It provides a systematic approach to moving from evidence to recommendations.

When to Use This Skill

Activate this skill when users:

• Ask about "quality of evidence" or "certainty"
• Need to create a Summary of Findings (SoF) table
• Want to understand GRADE ratings
• Ask about downgrading or upgrading evidence
• Are preparing a Cochrane review or guideline

GRADE Certainty Levels

Level	Symbol	Meaning
High	⊕⊕⊕⊕	Very confident the true effect is close to the estimate
Moderate	⊕⊕⊕◯	Moderately confident; true effect likely close to estimate
Low	⊕⊕◯◯	Limited confidence; true effect may be substantially different
Very Low	⊕◯◯◯	Very little confidence; true effect likely substantially different

Starting Point

Study Design	Starting Certainty
Randomized trials	High (⊕⊕⊕⊕)
Observational studies	Low (⊕⊕◯◯)

Factors That Lower Certainty (Downgrade)

1. Risk of Bias

What to assess:

• Randomization and allocation concealment
• Blinding of participants, personnel, outcome assessors
• Incomplete outcome data
• Selective reporting
• Other biases

When to downgrade:

• Serious limitations → Down 1 level
• Very serious limitations → Down 2 levels

Socratic Questions:

• "Were the studies properly randomized?"
• "Could the lack of blinding have affected results?"
• "Was there substantial loss to follow-up?"

2. Inconsistency (Heterogeneity)

What to assess:

• Point estimates vary widely
• Confidence intervals show minimal overlap
• I² is high
• Studies show different directions of effect

When to downgrade:

• Unexplained heterogeneity with I² > 50%
• Studies show conflicting results
• Prediction interval crosses null

Key Teaching Point: "Inconsistency is different from imprecision. Inconsistency means studies disagree; imprecision means we're uncertain about each estimate."

3. Indirectness

Types of indirectness:

Type	Example
Population	Studies in adults, question about children
Intervention	Studies of drug A, question about drug B
Comparator	Studies vs. placebo, question vs. active treatment
Outcome	Studies measure surrogate, question about clinical outcome

When to downgrade:

• Important differences between evidence and question
• Surrogate outcomes used instead of patient-important outcomes

4. Imprecision

What to assess:

• Wide confidence intervals
• Small sample size / few events
• Optimal Information Size (OIS) not met

Rules of thumb:

• Binary: < 300 events total → consider downgrading
• Continuous: < 400 participants total → consider downgrading
• CI crosses thresholds of clinical importance

When to downgrade:

• CI includes both appreciable benefit and appreciable harm
• CI includes no effect and appreciable benefit (or harm)

5. Publication Bias

What to assess:

• Funnel plot asymmetry
• Egger's test significant
• Industry funding with positive results only
• Small study effects

When to downgrade:

• Strong suspicion of missing studies
• Trim-and-fill suggests meaningful impact

Factors That Raise Certainty (Upgrade)

Only for observational studies starting at Low

1. Large Effect

Magnitude	Upgrade
RR > 2 or < 0.5	Consider +1
RR > 5 or < 0.2	Consider +2

2. Dose-Response Gradient

• Clear relationship between dose/exposure and outcome
• Biological plausibility

3. Plausible Confounding

• All plausible confounders would reduce the effect
• Yet effect is still observed

GRADE Assessment Process

Step 1: Define the Question (PICO)
    │
Step 2: Identify Study Designs
    │
    ├── RCTs → Start at HIGH
    └── Observational → Start at LOW
    │
Step 3: Assess Downgrade Factors
    │
    ├── Risk of Bias?
    ├── Inconsistency?
    ├── Indirectness?
    ├── Imprecision?
    └── Publication Bias?
    │
Step 4: Assess Upgrade Factors (if observational)
    │
    ├── Large Effect?
    ├── Dose-Response?
    └── Confounding?
    │
Step 5: Determine Final Rating
    │
Step 6: Write Certainty Statement

Summary of Findings Table

Template

Outcome	№ of studies (participants)	Certainty	Relative effect (95% CI)	Anticipated absolute effects
Mortality	5 RCTs (2,340)	⊕⊕⊕◯ Moderate	RR 0.75 (0.60-0.94)	50 fewer per 1000 (from 80 fewer to 12 fewer)

Creating Absolute Effects

# From relative risk
baseline_risk <- 0.20  # 20% in control group
RR <- 0.75
RR_lower <- 0.60
RR_upper <- 0.94

# Absolute risk reduction
ARR <- baseline_risk * (1 - RR)  # 5% = 50 per 1000
ARR_lower <- baseline_risk * (1 - RR_upper)
ARR_upper <- baseline_risk * (1 - RR_lower)

Certainty Statements

High certainty: "We are very confident that the true effect lies close to that of the estimate of the effect."

Moderate certainty: "We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different."

Low certainty: "Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect."

Very low certainty: "We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect."

Teaching Framework

Step 1: Establish the Question

"What exactly are we trying to answer? Let's define:

• Population
• Intervention
• Comparator
• Outcomes"

Step 2: Identify the Evidence

"What studies do we have?

• How many RCTs vs observational?
• What's our starting point?"

Step 3: Systematic Assessment

"Let's go through each GRADE domain:

1. First, risk of bias..."
2. Then, inconsistency..." [Continue through all domains]

Step 4: Make Judgments

"Based on our assessment:

• We downgraded for [reasons]
• Final certainty: [level]"

Step 5: Write the Statement

"Now let's write what this means for decision-makers..."

Common Mistakes to Avoid

1. Double-counting

   • Don't downgrade for both heterogeneity AND wide CIs if they're related

2. Automatic downgrading

   • Not every limitation requires downgrading
   • Consider impact on the effect estimate

3. Ignoring context

   • A "large" CI depends on clinical context
   • What difference matters to patients?

4. Forgetting outcomes

   • GRADE is assessed per outcome, not per review

Assessment Questions

1. Basic: "RCTs start at what GRADE certainty level?"

   • Correct: High

2. Intermediate: "I² = 70% with studies showing effects in opposite directions. Which domain is affected?"

   • Correct: Inconsistency

3. Advanced: "Studies are in adults but your question is about children. The intervention and outcomes are the same. What domain and how much to downgrade?"

   • Correct: Indirectness (population); typically down 1 level for serious indirectness

Related Skills

• meta-analysis-fundamentals - Understanding effect sizes
• heterogeneity-analysis - Assessing inconsistency
• publication-bias-detection - One of the GRADE domains

Adaptation Guidelines

Glass (the teaching agent) MUST adapt this content to the learner:

1. Language Detection: Detect the user's language from their messages and respond naturally in that language
2. Cultural Context: Adapt examples to local healthcare systems and research contexts when relevant
3. Technical Terms: Maintain standard English terms (e.g., "forest plot", "effect size", "I²") but explain them in the user's language
4. Level Adaptation: Adjust complexity based on user's demonstrated knowledge level
5. Socratic Method: Ask guiding questions in the detected language to promote deep understanding
6. Local Examples: When possible, reference studies or guidelines familiar to the user's region

Example Adaptations:

• 🇧🇷 Portuguese: Use Brazilian health system examples (SUS, ANVISA guidelines)
• 🇪🇸 Spanish: Reference PAHO/OPS guidelines for Latin America
• 🇨🇳 Chinese: Include examples from Chinese medical literature