--- name: scientific-systematic-review description: | PRISMA 2020 準拠系統的レビュースキル。マルチ DB 検索戦略立案 (PubMed/Embase/Cochrane/Web of Science)、スクリーニングワークフロー (タイトル/抄録→全文)、品質評価 (RoB 2/ROBINS-I/NOS)、データ抽出テンプレート、PRISMA フロー図自動生成パイプライン。 --- # Scientific Systematic Review PRISMA 2020 ガイドラインに準拠した系統的レビュー・メタアナリシスの方法論パイプラインを提供する。 ## When to Use - 系統的レビューの検索戦略を設計するとき - タイトル/抄録スクリーニングのワークフローが必要なとき - バイアスリスク (RoB 2, ROBINS-I, NOS) 評価を行うとき - PRISMA フロー図を生成するとき - 系統的レビューのデータ抽出テーブルを作成するとき --- ## Quick Start ## 1. 検索戦略設計 (PICO → クエリ) ```python import pandas as pd import json def design_search_strategy(pico, databases=None): """ PICO フレームワークから検索戦略を設計。 Parameters: pico: dict — {"P": "...", "I": "...", "C": "...", "O": "..."} databases: list — ["PubMed", "Embase", "Cochrane", "Web of Science"] """ if databases is None: databases = ["PubMed", "Embase", "Cochrane"] strategy = { "pico": pico, "databases": databases, "search_blocks": [], } # P (Population) ブロック p_terms = pico.get("P", "").split(",") p_block = { "concept": "Population", "terms": [t.strip() for t in p_terms], "mesh_terms": [], # 手動で MeSH を追加 "boolean": "OR", } # I (Intervention) ブロック i_terms = pico.get("I", "").split(",") i_block = { "concept": "Intervention", "terms": [t.strip() for t in i_terms], "mesh_terms": [], "boolean": "OR", } # C (Comparison) ブロック c_terms = pico.get("C", "").split(",") c_block = { "concept": "Comparison", "terms": [t.strip() for t in c_terms if t.strip()], "boolean": "OR", } # O (Outcome) ブロック o_terms = pico.get("O", "").split(",") o_block = { "concept": "Outcome", "terms": [t.strip() for t in o_terms], "boolean": "OR", } strategy["search_blocks"] = [p_block, i_block] if c_block["terms"]: strategy["search_blocks"].append(c_block) if o_block["terms"]: strategy["search_blocks"].append(o_block) # PubMed クエリ生成 pubmed_parts = [] for block in strategy["search_blocks"]: terms = [f'"{t}"' for t in block["terms"]] mesh = [f'"{m}"[MeSH]' for m in block.get("mesh_terms", [])] all_terms = terms + mesh pubmed_parts.append(f"({' OR '.join(all_terms)})") strategy["pubmed_query"] = " AND ".join(pubmed_parts) print(f"Search strategy: {len(strategy['search_blocks'])} blocks, " f"{len(databases)} databases") print(f"PubMed query: {strategy['pubmed_query'][:200]}...") return strategy ``` ## 2. スクリーニングワークフロー ```python def screening_workflow(records_df, stage="title_abstract", inclusion_criteria=None, exclusion_criteria=None): """ スクリーニングワークフロー管理。 Parameters: records_df: DataFrame — columns: [id, title, abstract, source] stage: "title_abstract" or "fulltext" inclusion_criteria: list — 適格基準 exclusion_criteria: list — 除外基準 """ if inclusion_criteria is None: inclusion_criteria = [ "Published in English or Japanese", "Human subjects", "Original research (not review/editorial)", ] if exclusion_criteria is None: exclusion_criteria = [ "Case reports (n < 5)", "Conference abstracts only", "Animal studies only", ] # 重複除去 initial_count = len(records_df) records_df = records_df.drop_duplicates(subset=["title"], keep="first") duplicates_removed = initial_count - len(records_df) # スクリーニング結果テンプレート records_df["decision"] = "pending" records_df["excluded_reason"] = "" records_df["screener"] = "" result = { "stage": stage, "total_records": initial_count, "duplicates_removed": duplicates_removed, "unique_records": len(records_df), "inclusion_criteria": inclusion_criteria, "exclusion_criteria": exclusion_criteria, } print(f"Screening ({stage}): {initial_count} records → " f"{duplicates_removed} duplicates removed → " f"{len(records_df)} to screen") return records_df, result ``` ## 3. バイアスリスク評価 ```python def risk_of_bias_assessment(studies_df, tool="RoB2"): """ バイアスリスク評価。 Parameters: studies_df: DataFrame — columns: [study_id, study_type, ...] tool: "RoB2" (RCT), "ROBINS-I" (非ランダム化), "NOS" (観察研究) """ if tool == "RoB2": # Cochrane RoB 2 — 5 ドメイン domains = [ "D1: Randomization process", "D2: Deviations from interventions", "D3: Missing outcome data", "D4: Measurement of the outcome", "D5: Selection of the reported result", ] levels = ["Low", "Some concerns", "High"] elif tool == "ROBINS-I": domains = [ "D1: Confounding", "D2: Selection of participants", "D3: Classification of interventions", "D4: Deviations from intended interventions", "D5: Missing data", "D6: Measurement of outcomes", "D7: Selection of the reported result", ] levels = ["Low", "Moderate", "Serious", "Critical", "NI"] elif tool == "NOS": domains = [ "Selection (0-4 stars)", "Comparability (0-2 stars)", "Outcome/Exposure (0-3 stars)", ] levels = ["0-3 (low quality)", "4-6 (moderate)", "7-9 (high quality)"] else: raise ValueError(f"Unknown tool: {tool}") # 評価テンプレート生成 assessments = [] for _, study in studies_df.iterrows(): assessment = { "study_id": study.get("study_id", ""), "tool": tool, } for domain in domains: assessment[domain] = "pending" assessment["overall"] = "pending" assessments.append(assessment) df = pd.DataFrame(assessments) print(f"RoB assessment ({tool}): {len(df)} studies, " f"{len(domains)} domains") return df ``` ## 4. PRISMA フロー図生成 ```python def generate_prisma_flowchart(counts, output="figures/prisma_flow.svg"): """ PRISMA 2020 フロー図の自動生成。 Parameters: counts: dict — { "databases": {"PubMed": 500, "Embase": 300, "Cochrane": 100}, "other_sources": 20, "duplicates_removed": 150, "title_abstract_screened": 770, "title_abstract_excluded": 650, "fulltext_assessed": 120, "fulltext_excluded": {"not_relevant": 30, "wrong_design": 20, ...}, "included_qualitative": 70, "included_quantitative": 50, } """ import os os.makedirs(os.path.dirname(output), exist_ok=True) # Mermaid 形式で PRISMA フロー生成 db_counts = counts.get("databases", {}) total_db = sum(db_counts.values()) other = counts.get("other_sources", 0) total = total_db + other dedup = counts.get("duplicates_removed", 0) screened = counts.get("title_abstract_screened", total - dedup) ta_excluded = counts.get("title_abstract_excluded", 0) ft_assessed = counts.get("fulltext_assessed", screened - ta_excluded) ft_excluded = counts.get("fulltext_excluded", {}) ft_excluded_total = sum(ft_excluded.values()) if isinstance(ft_excluded, dict) else ft_excluded qualitative = counts.get("included_qualitative", ft_assessed - ft_excluded_total) quantitative = counts.get("included_quantitative", qualitative) mermaid = f"""flowchart TD A[Database検索
n={total_db}] --> C[重複除去後
n={total - dedup}] B[その他ソース
n={other}] --> C C --> D[タイトル/抄録スクリーニング
n={screened}] D --> E[除外
n={ta_excluded}] D --> F[全文評価
n={ft_assessed}] F --> G[除外
n={ft_excluded_total}] F --> H[質的統合
n={qualitative}] H --> I[量的統合 (メタアナリシス)
n={quantitative}] """ # SVG として保存 (Mermaid CLI or fallback to text) mermaid_file = output.replace(".svg", ".mmd") with open(mermaid_file, "w") as f: f.write(mermaid) print(f"PRISMA flow: {total} identified → {qualitative} included") print(f" Mermaid source: {mermaid_file}") return mermaid_file, counts ``` ## 5. データ抽出テンプレート ```python def create_extraction_template(study_type="RCT", custom_fields=None): """ 系統的レビュー用データ抽出テンプレート。 Parameters: study_type: "RCT", "cohort", "cross-sectional", "case-control" custom_fields: list — 追加フィールド """ base_fields = [ "study_id", "first_author", "year", "country", "study_design", "sample_size", "population", "setting", ] if study_type == "RCT": type_fields = [ "intervention", "comparator", "randomization_method", "blinding", "follow_up_duration", "primary_outcome", "primary_result", "secondary_outcomes", "adverse_events", "attrition_rate", "itt_analysis", ] elif study_type == "cohort": type_fields = [ "exposure", "comparator", "follow_up_duration", "primary_outcome", "adjustment_variables", "effect_measure", "effect_estimate", "ci_95", "p_value", "loss_to_follow_up", ] else: type_fields = [ "exposure", "outcome", "adjustment_variables", "effect_measure", "effect_estimate", "ci_95", ] all_fields = base_fields + type_fields if custom_fields: all_fields.extend(custom_fields) template = pd.DataFrame(columns=all_fields) print(f"Extraction template ({study_type}): {len(all_fields)} fields") return template ``` ## References ### Output Files | ファイル | 形式 | |---|---| | `results/search_strategy.json` | JSON | | `results/screening_records.csv` | CSV | | `results/risk_of_bias.csv` | CSV | | `results/data_extraction.csv` | CSV | | `figures/prisma_flow.mmd` | Mermaid | | `figures/prisma_flow.svg` | SVG | ### 利用可能ツール > PubMed/EuropePMC ツールは `scientific-literature-search` スキルと共有。 | カテゴリ | 主要ツール | 用途 | |---|---|---| | PubMed | `PubMed_search_articles` | 系統的検索 | | PubMed | `PubMed_Guidelines_Search` | ガイドライン検索 | | EuropePMC | `EuropePMC_search_articles` | 欧州文献検索 | ### 参照スキル | スキル | 関連 | |---|---| | `scientific-literature-search` | マルチ DB 検索実行 | | `scientific-meta-analysis` | 量的統合 (Forest/Funnel プロット) | | `scientific-critical-review` | 品質評価・批判レビュー | | `scientific-academic-writing` | レビュー論文執筆 | | `scientific-scientific-schematics` | PRISMA 図作成 | ### 依存パッケージ `pandas`, `json` (stdlib)

scientific-metabolic-atlas

Scan your own AI components