Skill: aipoch-ai/graph-interpretation (version 0.1.0)

--- name: graph-interpretation description: Use when interpreting scientific graphs and charts, explaining data visualizations for research presentations, writing figure captions for publications, or analyzing trends in clinical research data. Converts complex visual data into clear, accurate explanations for academic papers, clinical reports, and public presentations. allowed-tools: "Read Write Bash Edit" license: MIT metadata: skill-author: AIPOCH version: "1.0" --- # Scientific Graph Interpreter Interpret and explain scientific graphs, charts, and data visualizations for research publications, clinical presentations, and academic communications with precision and clarity. ## Quick Start ```python from scripts.graph_interpreter import GraphInterpreter interpreter = GraphInterpreter() # Comprehensive graph analysis analysis = interpreter.interpret( image_path="figure_1.png", graph_type="kaplan_meier", context="oncology_phase3_trial", audience="clinicians" ) print(analysis.statistical_summary) print(analysis.clinical_significance) print(analysis.suggested_caption) ``` ## Core Capabilities ### 1. Multi-Type Graph Analysis ```python analysis = interpreter.analyze( graph_type="forest_plot", data={ "studies": ["Study A", "Study B", "Study C"], "effect_sizes": [1.2, 0.8, 1.5], "confidence_intervals": [[1.0, 1.4], [0.6, 1.0], [1.2, 1.8]], "overall_effect": 1.15, "heterogeneity_p": 0.04 } ) ``` **Supported Graph Types:** | Graph Type | Common Use | Key Elements to Extract | |------------|------------|------------------------| | **Kaplan-Meier** | Survival analysis | Median survival, HR, 95% CI, log-rank p | | **Forest Plot** | Meta-analysis | Effect size, CI, heterogeneity (I²), weights | | **ROC Curve** | Diagnostic accuracy | AUC, sensitivity, specificity, optimal cutoff | | **Box Plot** | Distribution comparison | Median, IQR, outliers, whiskers | | **Scatter Plot** | Correlation | R², p-value, trend line, outliers | | **Bar Chart** | Group comparisons | Means, SEM/SD, significance indicators | | **Heatmap** | Expression/omics | Scale, clustering, row/column annotations | | **Volcano Plot** | Differential analysis | Fold change, p-value, FDR threshold | ### 2. Statistical Interpretation ```python stats = interpreter.extract_statistics( graph_data, extract=[ "p_values", "confidence_intervals", "effect_sizes", "sample_sizes", "statistical_tests" ] ) ``` **Statistical Reporting Standards:** ```python # Example output structure { "primary_outcome": { "measure": "Hazard Ratio", "value": 0.72, "ci_95": [0.58, 0.89], "p_value": 0.003, "interpretation": "32% risk reduction" }, "secondary_outcomes": [...], "significance_level": 0.05, "multiple_comparison_adjusted": True } ``` ### 3. Audience-Specific Explanations ```python explanations = interpreter.generate_multi_audience( analysis, audiences=["researchers", "clinicians", "patients", "policy_makers"] ) ``` **Explanation Templates:** **For Researchers:** > "The Kaplan-Meier analysis demonstrates a statistically significant > survival advantage for the experimental arm (HR 0.72, 95% CI 0.58-0.89, > p=0.003). Median survival improved from 14.2 to 19.6 months. > The proportional hazards assumption was verified (p=0.42)." **For Clinicians:** > "This trial shows patients on the new treatment lived about 5 months > longer on average compared to standard care. The 32% reduction in > death risk is significant and clinically meaningful. Consider this > option for eligible patients." **For Patients:** > "The study found that people taking the new treatment lived longer > than those on standard treatment. About 1 in 3 patients benefited > from the new treatment. Side effects were manageable." ### 4. Figure Caption Generation ```python caption = interpreter.generate_caption( analysis, style="journal", # or "presentation", "poster" word_limit=250, include_statistics=True ) ``` **Caption Structure:** ``` Figure X. [Brief title]. [What is shown: X-axis shows..., Y-axis shows..., lines/bars represent...]. [Key finding: Group A showed... compared to Group B...]. [Statistics: HR 0.72 (95% CI 0.58-0.89), p=0.003]. [Conclusion: This demonstrates...]. ``` ### 5. Critical Appraisal ```python appraisal = interpreter.critical_appraisal( graph_data, check=[ "appropriate_graph_type", "axis_scaling", "error_bars_present", "sample_size_adequate", "confounding_controlled", "generalizability" ] ) ``` **Common Graph Pitfalls:** | Issue | Problem | Better Approach | |-------|---------|-----------------| | Truncated y-axis | Exaggerates differences | Start at 0 or clearly indicate break | | No error bars | Hides variability | Include SD, SEM, or 95% CI | | 3D effects | Distorts perception | Use 2D with clear labels | | Dual y-axes | Confusing comparison | Separate graphs or normalized scale | | p-hacking indicators | Multiple comparisons | Adjusted p-values, Bonferroni | ## CLI Usage ```bash # Comprehensive analysis python scripts/graph_interpreter.py \ --image survival_curve.png \ --type kaplan_meier \ --context "phase_3_oncology" \ --audience clinicians \ --output analysis.json # Generate publication caption python scripts/graph_interpreter.py \ --image forest_plot.png \ --type forest_plot \ --generate caption \ --journal-style nature \ --word-limit 200 # Batch process figures python scripts/graph_interpreter.py \ --batch figures/ \ --output report.html \ --template comprehensive ``` ## Common Patterns ### Pattern 1: Clinical Trial Primary Endpoint ```python # Analyze survival curve analysis = interpreter.interpret( graph_type="kaplan_meier", primary_endpoint="overall_survival", treatment_arms=["Experimental", "Control"], key_metrics=["median_os", "hr", "ci", "p_value"] ) # Generate regulatory-ready summary regulatory_summary = interpreter.generate_regulatory_summary( analysis, guideline="ICH_E3" ) ``` ### Pattern 2: Meta-Analysis Forest Plot ```python # Interpret meta-analysis analysis = interpreter.interpret_forest_plot( studies=included_studies, check_heterogeneity=True, assess_publication_bias=True ) # Generate GRADE assessment grade_rating = interpreter.generate_grade_rating(analysis) ``` ### Pattern 3: Diagnostic Accuracy ROC ```python # Analyze diagnostic test analysis = interpreter.interpret_roc( curves=["Test A", "Test B", "Combined"], optimal_cutoffs=True, clinical Utility=True ) # Clinical decision support decision_aid = interpreter.generate_decision_aid(analysis) ``` ## Quality Checklist **Before Interpretation:** - [ ] Graph type appropriate for data - [ ] Axes clearly labeled with units - [ ] Sample sizes indicated - [ ] Statistical tests specified - [ ] Confidence intervals present **During Interpretation:** - [ ] Effect size calculated - [ ] Clinical significance assessed - [ ] Confidence intervals interpreted - [ ] Limitations noted - [ ] Generalizability considered **After Interpretation:** - [ ] Explanation appropriate for audience - [ ] Statistical terms explained - [ ] Uncertainty communicated - [ ] Actionable insights highlighted ## Best Practices **Statistical Communication:** - Always report confidence intervals with point estimates - Distinguish statistical from clinical significance - Note limitations and generalizability - Avoid causal language in observational studies **Visual Analysis:** - Check axis scales for distortion - Note truncated axes or breaks - Identify outliers and their impact - Verify error bar representation (SD vs SEM) ## Common Pitfalls ❌ **Correlation = Causation**: "X causes Y because they're correlated" ✅ **Cautious Interpretation**: "X is associated with Y; other factors may explain this" ❌ **Overstating Significance**: "Highly significant (p<0.001)" as meaning large effect ✅ **Proper Framing**: "Statistically significant but modest effect size (d=0.2)" ❌ **Ignoring Confidence Intervals**: Reporting point estimate only ✅ **Interval Reporting**: "Effect: 1.5 (95% CI: 0.9-2.4), suggesting uncertainty" --- **Skill ID**: 209 | **Version**: 1.0 | **License**: MIT


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