"""Report generation for triangulation analyses. Uses python-docx if available; otherwise produces a markdown report. Imports are intentionally lazy so the CLI works without python-docx installed. """ from __future__ import annotations from typing import Any DISCLAIMER = ( "본 도구는 연구 가설 생성·문헌 갭 분석 목적의 연구용·교육용 도구입니다. " "임상의사결정·환자 진료에 직접 사용해서는 안 되며, 모든 분석 결과는 추가 검증이 필요합니다." ) def build_markdown_report( pair: tuple[str, str], pair_effects: list[dict[str, Any]], concordance: dict[str, Any], lawlor: dict[str, Any], biases: list[dict[str, Any]], designs: list[dict[str, Any]], ) -> str: drug_class, outcome = pair lines: list[str] = [] lines.append(f"# Triangulation Report — {drug_class} × {outcome}") lines.append("") lines.append(f"> {DISCLAIMER}") lines.append("") lines.append("## 1. 5-design effect grid") lines.append("") lines.append("| design | drug | effect | 95% CI | n | f/u (y) | source |") lines.append("| --- | --- | ---: | --- | ---: | ---: | --- |") for r in pair_effects: ci = ( f"{r.get('ci_low')}–{r.get('ci_high')}" if r.get("ci_low") is not None and r.get("ci_high") is not None else "NA" ) lines.append( f"| {r.get('design','')} | {r.get('drug','')} | " f"{r.get('effect_estimate','')} | {ci} | " f"{r.get('sample_size') or 'NA'} | {r.get('follow_up_years') or 'NA'} | " f"{r.get('source_citation','')} |" ) lines.append("") lines.append("## 2. Concordance metric") lines.append("") for k, v in concordance.items(): lines.append(f"- {k}: {v}") lines.append("") lines.append("## 3. Lawlor 2016 5-criterion (each 0–2)") lines.append("") for c in lawlor["criteria"]: lines.append(f"- **{c['name']}**: {c['score']}/{c['max']} — {c['rationale']}") lines.append( f"- **total**: {lawlor['total']}/{lawlor['max']} " f"(normalized {lawlor['normalized']})" ) lines.append("") lines.append("## 4. Discordance bias direction (ranked)") lines.append("") if not biases: lines.append("No discordance pattern triggered.") else: for b in biases: lines.append( f"- **{b['bias_type']}** (score {b['score']}): {b['rationale']}" ) if b.get("definition"): lines.append(f" - 정의: {b['definition']}") lines.append("") lines.append("## 5. Triangulation-targeted follow-up design cards") lines.append("") for i, c in enumerate(designs, 1): lines.append(f"### {i}. {c['name']} (match score {c['match_score']})") lines.append(f"- primary endpoint: {c['primary_endpoint']}") lines.append(f"- estimated n: {c['estimated_sample_size']}") lines.append(f"- follow-up: {c['follow_up_years']} years") lines.append(f"- addresses biases: {', '.join(c['addresses_biases'])}") lines.append(f"- key hypothesis: {c['key_hypothesis']}") lines.append(f"- rationale: {c['rationale']}") lines.append("") return "\n".join(lines) def build_docx_report( pair: tuple[str, str], pair_effects: list[dict[str, Any]], concordance: dict[str, Any], lawlor: dict[str, Any], biases: list[dict[str, Any]], designs: list[dict[str, Any]], out_path: str, ) -> str: """Write a docx report. Requires python-docx; falls back to markdown. Returns the actual path written (may be .md if docx unavailable). """ try: from docx import Document # type: ignore except Exception: # Fallback: write a markdown file with the same stem md = build_markdown_report(pair, pair_effects, concordance, lawlor, biases, designs) if out_path.lower().endswith(".docx"): out_path = out_path[:-5] + ".md" with open(out_path, "w", encoding="utf-8") as f: f.write(md) return out_path drug_class, outcome = pair doc = Document() doc.add_heading(f"Triangulation Report — {drug_class} × {outcome}", level=1) p = doc.add_paragraph() p.add_run(DISCLAIMER).italic = True doc.add_heading("1. 5-design effect grid", level=2) table = doc.add_table(rows=1, cols=7) hdr = table.rows[0].cells for i, h in enumerate(["design", "drug", "effect", "95% CI", "n", "f/u (y)", "source"]): hdr[i].text = h for r in pair_effects: cells = table.add_row().cells cells[0].text = str(r.get("design", "")) cells[1].text = str(r.get("drug", "")) cells[2].text = str(r.get("effect_estimate", "")) cells[3].text = ( f"{r.get('ci_low')}–{r.get('ci_high')}" if r.get("ci_low") is not None and r.get("ci_high") is not None else "NA" ) cells[4].text = str(r.get("sample_size") or "NA") cells[5].text = str(r.get("follow_up_years") or "NA") cells[6].text = str(r.get("source_citation", "")) doc.add_heading("2. Concordance metric", level=2) for k, v in concordance.items(): doc.add_paragraph(f"{k}: {v}", style="List Bullet") doc.add_heading("3. Lawlor 2016 5-criterion", level=2) for c in lawlor["criteria"]: doc.add_paragraph( f"{c['name']}: {c['score']}/{c['max']} — {c['rationale']}", style="List Bullet", ) doc.add_paragraph( f"total: {lawlor['total']}/{lawlor['max']} (normalized {lawlor['normalized']})" ) doc.add_heading("4. Discordance bias direction (ranked)", level=2) if not biases: doc.add_paragraph("No discordance pattern triggered.") else: for b in biases: doc.add_paragraph( f"{b['bias_type']} (score {b['score']}): {b['rationale']}", style="List Bullet", ) doc.add_heading("5. Follow-up design cards", level=2) for i, c in enumerate(designs, 1): doc.add_heading(f"{i}. {c['name']} (match {c['match_score']})", level=3) doc.add_paragraph(f"primary endpoint: {c['primary_endpoint']}") doc.add_paragraph(f"estimated n: {c['estimated_sample_size']}") doc.add_paragraph(f"follow-up: {c['follow_up_years']} years") doc.add_paragraph(f"addresses biases: {', '.join(c['addresses_biases'])}") doc.add_paragraph(f"key hypothesis: {c['key_hypothesis']}") doc.add_paragraph(f"rationale: {c['rationale']}") doc.save(out_path) return out_path