Use this Skill when conducting a systematic literature review following PRISMA 2020: PICO framework, database search strategy, title/abstract screening, full-text eligibility, data extraction, and GRADE evidence grading.
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Use this Skill when conducting a systematic literature review following PRISMA 2020: PICO framework, database search strategy, title/abstract screening, full-text eligibility, data extraction, and GRADE evidence grading.
TL;DR — Conduct a rigorous systematic review using the PRISMA 2020 framework:
build PICO-based search queries, search multiple databases via API, deduplicate
records, run two-stage screening, extract data into a structured DataFrame, apply
GRADE evidence grading, and generate the PRISMA flow diagram automatically.
When to Use This Skill
Use this Skill whenever you need to:
Summarize evidence on a clinical, policy, or scientific question following PRISMA 2020
Build reproducible, documented search strategies across PubMed, Embase, Cochrane, or Scopus
Track the screening process and record reasons for exclusion at each stage
Extract structured data from included studies into a machine-readable format
Grade the certainty of evidence using the GRADE framework
Generate the mandatory PRISMA flow diagram for journal submission
Task
When to apply
Database search
Need comprehensive evidence retrieval
Deduplication
After merging records from ≥2 databases
Title/abstract screening
First-pass filter on thousands of records
Full-text eligibility
Detailed inclusion/exclusion assessment
Data extraction
Populate the evidence table
GRADE grading
Communicate certainty to clinicians or policymakers
Background & Key Concepts
PRISMA 2020
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020
defines a four-stage flowchart:
Identification — Records retrieved from databases + other sources
Screening — Records after deduplication; title/abstract screened; reasons for exclusion
Eligibility — Full-text assessed; reasons for exclusion recorded
Included — Studies included in the review (and meta-analysis if applicable)
PICO Framework
Element
Definition
Example
Population
Who are the subjects?
Adults with type-2 diabetes
Intervention
What is being tested?
SGLT2 inhibitor
Comparator
What is the control?
Placebo or standard care
Outcome
What is measured?
HbA1c reduction at 24 weeks
GRADE Evidence Certainty
Level
Meaning
Typical study type
High
True effect is close to estimate
Well-conducted RCTs
Moderate
Moderate confidence; true effect likely similar
RCTs with limitations
Low
Limited confidence
Observational studies
Very Low
Very uncertain
Case series, expert opinion
GRADE certainty starts at "High" for RCTs and can be downgraded for risk of bias,
inconsistency, indirectness, imprecision, and publication bias.
For PubMed API access, register for an NCBI API key to raise the rate limit from
3 to 10 requests per second:
# Register at: https://www.ncbi.nlm.nih.gov/account/# Then set the environment variable:export NCBI_API_KEY="<paste-your-key>"# Verify
python -c "import os; print(os.getenv('NCBI_API_KEY', 'NOT SET'))"
Core Workflow
Step 1 — Build Search Strategy and Query Databases
The following code implements a PubMed search using the NCBI E-utilities API.
It constructs a PICO-based Boolean query and retrieves PubMed IDs (PMIDs) along
with article metadata.
import os
import time
import requests
import pandas as pd
from typing importList, Optional# export NCBI_API_KEY="<paste-your-key>"
NCBI_API_KEY: Optional[str] = os.getenv("NCBI_API_KEY")
NCBI_BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"defbuild_pico_query(
population: List[str],
intervention: List[str],
comparator: Optional[List[str]] = None,
outcome: Optional[List[str]] = None,
study_filters: Optional[List[str]] = None,
) -> str:
"""
Build a PubMed Boolean search string from PICO components.
Each list element within a PICO component is joined with OR;
the components are joined with AND.
Args:
population: MeSH terms / free-text for the target population.
intervention: MeSH terms / free-text for the intervention.
comparator: MeSH terms / free-text for the comparator (optional).
outcome: MeSH terms / free-text for the outcome (optional).
study_filters: Filters such as 'Randomized Controlled Trial[pt]'.
Returns:
PubMed query string suitable for the esearch endpoint.
Example:
>>> q = build_pico_query(
... population=["type 2 diabetes[MeSH]", "T2DM"],
... intervention=["SGLT2 inhibitor[MeSH]", "empagliflozin", "dapagliflozin"],
... outcome=["HbA1c", "glycated hemoglobin"],
... study_filters=["Randomized Controlled Trial[pt]"],
... )
"""defjoin_component(terms: List[str]) -> str:
return"(" + " OR ".join(terms) + ")"
parts = [join_component(population), join_component(intervention)]
if comparator:
parts.append(join_component(comparator))
if outcome:
parts.append(join_component(outcome))
if study_filters:
parts.append(join_component(study_filters))
return" AND ".join(parts)
defsearch_pubmed(
query: str,
max_results: int = 500,
date_range: Optional[tuple] = None,
) -> List[str]:
"""
Search PubMed via E-utilities and return a list of PMIDs.
Args:
query: Boolean PubMed query string.
max_results: Maximum number of PMIDs to retrieve.
date_range: Optional (min_date, max_date) tuple, format 'YYYY/MM/DD'.
Returns:
List of PMID strings.
"""
params = {
"db": "pubmed",
"term": query,
"retmax": max_results,
"retmode": "json",
"usehistory": "y",
}
if NCBI_API_KEY:
params["api_key"] = NCBI_API_KEY
if date_range:
params["mindate"], params["maxdate"] = date_range
params["datetype"] = "pdat"
resp = requests.get(f"{NCBI_BASE}/esearch.fcgi", params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
pmids = data["esearchresult"]["idlist"]
total = int(data["esearchresult"]["count"])
print(f"PubMed: {total} total hits; retrieved {len(pmids)} PMIDs.")
return pmids
deffetch_pubmed_summaries(pmids: List[str], batch_size: int = 100) -> pd.DataFrame:
"""
Fetch article metadata for a list of PMIDs using eSummary.
Args:
pmids: List of PMID strings.
batch_size: Number of IDs per API request (max 200 recommended).
Returns:
DataFrame with columns: pmid, title, authors, journal, year, abstract_flag.
"""
records = []
for i inrange(0, len(pmids), batch_size):
batch = pmids[i : i + batch_size]
params = {
"db": "pubmed",
"id": ",".join(batch),
"retmode": "json",
}
if NCBI_API_KEY:
params["api_key"] = NCBI_API_KEY
resp = requests.get(f"{NCBI_BASE}/esummary.fcgi", params=params, timeout=30)
resp.raise_for_status()
result = resp.json().get("result", {})
for pmid in batch:
art = result.get(pmid, {})
authors = "; ".join(
a.get("name", "") for a in art.get("authors", [])
)
records.append({
"pmid": pmid,
"title": art.get("title", ""),
"authors": authors,
"journal": art.get("fulljournalname", ""),
"year": art.get("pubdate", "")[:4],
"source": "PubMed",
"screen_status": "pending",
})
time.sleep(0.15) # respect rate limitreturn pd.DataFrame(records)
# ── Usage example ────────────────────────────────────────────────────────────if __name__ == "__main__":
query = build_pico_query(
population=["type 2 diabetes[MeSH]", "T2DM"],
intervention=["SGLT2 inhibitor[MeSH]", "empagliflozin", "dapagliflozin"],
outcome=["HbA1c", "glycated hemoglobin"],
study_filters=["Randomized Controlled Trial[pt]"],
)
print("Query:", query)
pmids = search_pubmed(query, max_results=200)
df_pubmed = fetch_pubmed_summaries(pmids)
df_pubmed.to_csv("pubmed_results.csv", index=False)
print(df_pubmed.head())
Step 2 — Deduplicate and Screen Records
After merging results from multiple databases, remove duplicates by title similarity
and track screening decisions in a structured DataFrame.
import hashlib
import re
import pandas as pd
from typing importDictdefnormalize_title(title: str) -> str:
"""Lowercase, strip punctuation, collapse whitespace for fuzzy matching."""
title = title.lower()
title = re.sub(r"[^\w\s]", "", title)
title = re.sub(r"\s+", " ", title).strip()
return title
defdeduplicate_records(dfs: Dict[str, pd.DataFrame]) -> pd.DataFrame:
"""
Merge records from multiple databases and deduplicate by normalized title.
Args:
dfs: Dictionary mapping source name -> DataFrame with at least 'title' column.
Returns:
Deduplicated DataFrame with a 'duplicate_removed' flag column and
PRISMA stage counts printed to stdout.
"""
combined = pd.concat(list(dfs.values()), ignore_index=True)
total_retrieved = len(combined)
print(f"PRISMA Stage 1 — Identification: {total_retrieved} records retrieved")
# Generate a hash key from the normalized title
combined["title_norm"] = combined["title"].fillna("").apply(normalize_title)
combined["title_hash"] = combined["title_norm"].apply(
lambda t: hashlib.md5(t.encode()).hexdigest()
)
# Keep first occurrence of each title hash
combined["duplicate_removed"] = combined.duplicated(subset="title_hash", keep="first")
n_duplicates = combined["duplicate_removed"].sum()
n_after_dedup = total_retrieved - n_duplicates
print(f"PRISMA Stage 2a — Deduplication: {n_duplicates} duplicates removed; "f"{n_after_dedup} records remain")
combined["screen_status"] = "pending"
combined["exclude_reason"] = ""return combined.reset_index(drop=True)
defapply_title_abstract_screen(
df: pd.DataFrame,
include_keywords: list,
exclude_keywords: list,
) -> pd.DataFrame:
"""
Automated title/abstract screening by keyword matching.
In real practice, two independent reviewers screen manually and resolve
disagreements. This function demonstrates automated pre-screening for
efficiency in large retrieval sets.
Args:
df: Combined, deduplicated records DataFrame.
include_keywords: Records MUST match at least one of these.
exclude_keywords: Records are excluded if they match any of these.
Returns:
DataFrame with 'screen_status' updated to 'include', 'exclude', or 'unclear'.
"""
df = df.copy()
search_text = (df["title"].fillna("") + " " + df.get("abstract", pd.Series([""] * len(df)))).str.lower()
has_include = search_text.apply(
lambda t: any(kw.lower() in t for kw in include_keywords)
)
has_exclude = search_text.apply(
lambda t: any(kw.lower() in t for kw in exclude_keywords)
)
df.loc[df["duplicate_removed"], "screen_status"] = "duplicate"
df.loc[~df["duplicate_removed"] & ~has_include, "screen_status"] = "exclude"
df.loc[~df["duplicate_removed"] & ~has_include, "exclude_reason"] = "no relevant keywords"
df.loc[~df["duplicate_removed"] & has_exclude, "screen_status"] = "exclude"
df.loc[~df["duplicate_removed"] & has_exclude, "exclude_reason"] = "exclusion keyword match"
df.loc[~df["duplicate_removed"] & has_include & ~has_exclude, "screen_status"] = "include"
counts = df["screen_status"].value_counts()
print(f"PRISMA Stage 2b — Title/Abstract Screen:\n{counts.to_string()}")
return df
# ── Screening tracker ─────────────────────────────────────────────────────────defcreate_screening_tracker(df: pd.DataFrame, output_path: str = "screening_tracker.csv") -> None:
"""Save the screening DataFrame with all PRISMA stage metadata."""
cols = ["pmid", "title", "authors", "year", "journal", "source",
"duplicate_removed", "screen_status", "exclude_reason"]
available = [c for c in cols if c in df.columns]
df[available].to_csv(output_path, index=False)
print(f"Screening tracker saved to {output_path}")
Step 3 — Data Extraction, GRADE Grading, and PRISMA Flow Diagram
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import pandas as pd
from typing importLiteral# ── Data extraction template ──────────────────────────────────────────────────
EXTRACTION_COLUMNS = [
"study_id", "author", "year", "country", "design", "n_total",
"n_intervention", "n_control", "population_description",
"intervention_description", "comparator_description",
"primary_outcome", "effect_size", "ci_lower", "ci_upper",
"p_value", "follow_up_weeks", "risk_of_bias", "grade_certainty",
"notes",
]
defcreate_extraction_template(output_path: str = "data_extraction.csv") -> pd.DataFrame:
"""Return an empty DataFrame ready for manual data extraction."""
df = pd.DataFrame(columns=EXTRACTION_COLUMNS)
df.to_csv(output_path, index=False)
print(f"Data extraction template saved to {output_path}")
return df
defgrade_evidence(
study_design: Literal["RCT", "cohort", "case-control", "cross-sectional", "case-series"],
risk_of_bias: Literal["low", "some concerns", "high"],
inconsistency: bool = False,
indirectness: bool = False,
imprecision: bool = False,
publication_bias: bool = False,
) -> str:
"""
Apply GRADE evidence grading rules.
Starting certainty: High for RCTs, Low for observational studies.
Downgrade one level for each serious concern.
Args:
study_design: Study design type.
risk_of_bias: Overall risk of bias assessment.
inconsistency: True if I² > 50% or unexplained heterogeneity.
indirectness: True if PICO differs importantly from review question.
imprecision: True if confidence intervals are wide or n is small.
publication_bias: True if funnel plot asymmetry or selective reporting.
Returns:
GRADE certainty label: 'High', 'Moderate', 'Low', or 'Very Low'.
"""
LEVELS = ["Very Low", "Low", "Moderate", "High"]
start = 3if study_design == "RCT"else1# High vs Low
downgrades = 0if risk_of_bias in ("some concerns",):
downgrades += 1elif risk_of_bias == "high":
downgrades += 2if inconsistency:
downgrades += 1if indirectness:
downgrades += 1if imprecision:
downgrades += 1if publication_bias:
downgrades += 1
final_idx = max(0, start - downgrades)
return LEVELS[final_idx]
defplot_prisma_flowchart(
n_identified: int,
n_duplicates: int,
n_title_excluded: int,
n_fulltext_assessed: int,
n_fulltext_excluded: int,
n_included: int,
output_path: str = "prisma_flowchart.png",
) -> None:
"""
Generate a PRISMA 2020 flow diagram using matplotlib.
Args:
n_identified: Total records identified across all databases.
n_duplicates: Duplicate records removed.
n_title_excluded: Records excluded at title/abstract screen.
n_fulltext_assessed: Records assessed for full-text eligibility.
n_fulltext_excluded: Full-text articles excluded with reasons.
n_included: Studies included in the review.
output_path: File path to save the PNG diagram.
"""
n_screened = n_identified - n_duplicates
fig, ax = plt.subplots(figsize=(10, 14))
ax.set_xlim(0, 10)
ax.set_ylim(0, 14)
ax.axis("off")
ax.set_facecolor("white")
defbox(x, y, w, h, text, color="#D6E4F0"):
rect = mpatches.FancyBboxPatch(
(x - w / 2, y - h / 2), w, h,
boxstyle="round,pad=0.1",
facecolor=color, edgecolor="#2C5F8A", linewidth=1.5,
)
ax.add_patch(rect)
ax.text(x, y, text, ha="center", va="center", fontsize=9,
wrap=True, multialignment="center")
defarrow(x1, y1, x2, y2):
ax.annotate("", xy=(x2, y2), xytext=(x1, y1),
arrowprops=dict(arrowstyle="->", color="#2C5F8A", lw=1.5))
# Identification
box(5, 13, 6, 1.0,
f"Records identified from databases\n(n = {n_identified})")
# Deduplication
box(5, 11.2, 6, 1.0,
f"Records after duplicates removed\n(n = {n_screened})\nDuplicates removed: {n_duplicates}")
arrow(5, 12.5, 5, 11.7)
# Screening
box(5, 9.3, 6, 1.0,
f"Records screened\n(n = {n_screened})")
box(8.5, 9.3, 2.5, 1.0,
f"Excluded\n(n = {n_title_excluded})",
color="#FAD7A0")
arrow(5, 10.7, 5, 9.8)
ax.annotate("", xy=(7.2, 9.3), xytext=(6.2, 9.3), # sideways
arrowprops=dict(arrowstyle="->", color="#2C5F8A", lw=1.5))
# Eligibility
box(5, 7.3, 6, 1.0,
f"Full-text articles assessed\n(n = {n_fulltext_assessed})")
box(8.5, 7.3, 2.5, 1.0,
f"Excluded\n(n = {n_fulltext_excluded})",
color="#FAD7A0")
arrow(5, 8.8, 5, 7.8)
ax.annotate("", xy=(7.2, 7.3), xytext=(6.2, 7.3),
arrowprops=dict(arrowstyle="->", color="#2C5F8A", lw=1.5))
# Included
box(5, 5.3, 6, 1.0,
f"Studies included in review\n(n = {n_included})",
color="#D5F5E3")
arrow(5, 6.8, 5, 5.8)
ax.set_title("PRISMA 2020 Flow Diagram", fontsize=13, fontweight="bold", pad=10)
fig.tight_layout()
fig.savefig(output_path, dpi=150, bbox_inches="tight")
print(f"PRISMA flowchart saved to {output_path}")
# ── End-to-end demo ───────────────────────────────────────────────────────────if __name__ == "__main__":
# Simulate PRISMA stage counts
plot_prisma_flowchart(
n_identified=1842,
n_duplicates=324,
n_title_excluded=1287,
n_fulltext_assessed=231,
n_fulltext_excluded=198,
n_included=33,
output_path="prisma_flowchart.png",
)
# Create extraction template
tmpl = create_extraction_template("data_extraction.csv")
# Example GRADE assessment
certainty = grade_evidence(
study_design="RCT",
risk_of_bias="some concerns",
inconsistency=True,
imprecision=False,
)
print(f"GRADE certainty: {certainty}") # -> Moderate
Advanced Usage
Multi-Database Search Strategy
For a comprehensive systematic review, search at least three databases. Below are
representative query translations for the same PICO question across platforms:
Database
Query syntax
Access method
PubMed
MeSH terms + Boolean
E-utilities REST API (see Step 1)
Embase
Emtree terms + .de. tags
Institutional Elsevier API
Cochrane
MeSH + free-text
Cochrane REST API or manual export
Scopus
TITLE-ABS-KEY()
Elsevier Scopus API
Sample Scopus query (same PICO):
TITLE-ABS-KEY(
("type 2 diabetes" OR "T2DM")
AND ("SGLT2 inhibitor" OR "empagliflozin" OR "dapagliflozin")
AND ("HbA1c" OR "glycated hemoglobin")
AND ("randomized controlled trial" OR "RCT")
)
AND PUBYEAR > 2010
AND DOCTYPE(ar)
Risk of Bias Assessment
For RCTs, use the Cochrane RoB 2.0 tool. Assess five domains:
Randomization process
Deviations from intended intervention
Missing outcome data
Measurement of the outcome
Selection of the reported result
Store judgements in the extraction DataFrame under risk_of_bias column
('low', 'some concerns', or 'high').
GRADE Evidence Profile Table
After extracting data and grading certainty, summarize findings in a GRADE
evidence profile table. Columns: Outcome, No. studies, No. participants,
Relative effect (95% CI), Absolute effect, Certainty, Importance.
Troubleshooting
Problem
Likely cause
Fix
requests.HTTPError: 429
PubMed rate limit exceeded
Set NCBI_API_KEY; add time.sleep(0.15) between requests
KeyError: 'esearchresult'
Empty query or API outage
Validate query string; retry with exponential back-off
Very high deduplication rate
Overly broad query
Tighten MeSH terms; add study-type filter
Very low retrieval
Overly narrow query
Broaden with OR synonyms; add free-text variants
matplotlib box overlap
Too many PRISMA stages shown
Adjust figsize and y-coordinates in plot_prisma_flowchart
Inconsistent GRADE ratings
Subjective domain judgments
Use the official GRADEpro GDT web tool for consensus grading
