import pandas as pd
 
# Data from Table S2
data = {
    "Domain": [
        "Q1. Functional unit defined",
        "Q2. System boundaries specified",
        "Q3. Stage exclusions justified",
        "Q4. Data sources specified",
        "Q5. Geographic relevance",
        "Q6. Technology data up-to-date",
        "Q7. Key assumptions listed",
        "Q8. LCIA method identified",
        "Q9. Sensitivity/uncertainty analysis",
        "Q10. Conclusions supported",
        "Q11. Funding/conflicts disclosed"
    ],
    "Yes": [84.21, 100.00, 0.00, 94.74, 92.11, 81.58, 84.21, 39.47, 57.89, 92.11, 100.00],
    "Partly": [15.79, 0.00, 2.63, 5.26, 5.26, 18.42, 10.53, 2.63, 10.53, 5.26, 0.00],
    "No": [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 5.26, 57.89, 26.32, 2.63, 0.00],
    "Not Reported": [0.00, 0.00, 97.37, 0.00, 2.63, 0.00, 0.00, 0.00, 5.26, 0.00, 0.00],
}
 
df = pd.DataFrame(data)
 
# Convert percentages to study counts (out of 38)
df["Yes_count"] = (df["Yes"] * 38 / 100).round().astype(int)
df["Partly_count"] = (df["Partly"] * 38 / 100).round().astype(int)
df["No_count"] = (df["No"] * 38 / 100).round().astype(int)
df["NR_count"] = (df["Not Reported"] * 38 / 100).round().astype(int)
 
# Calculate average score per question using scoring rule (Yes=1, Partly=0.5, No/NR=0)
df["Avg_score"] = (
    (df["Yes_count"] * 1 + df["Partly_count"] * 0.5) / 38
).round(2)
 
# Calculate total possible score = 11
# Average study-level quality score (sum of avg_scores across 11 domains)
total_avg_score = df["Avg_score"].sum().round(2)
 
# Classification
if total_avg_score >= 9:
    quality = "High quality"
elif total_avg_score >= 6:
    quality = "Moderate quality"
else:
    quality = "Low quality"
 
import caas_jupyter_tools
caas_jupyter_tools.display_dataframe_to_user("Risk of Bias Scoring Table", df)
 
(total_avg_score, quality)
 

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