// EvidenceGrid.jsx — 3×3 evidence wall with real papers
// Real journal names lifted from medical-data-discovery/prototype (evidenceCards).
// Impact factors are the publicly-reported JCR 2023/2024 values — verify annually.
const EVIDENCE = [
  {
    journal: "BMJ", impact: "105.7", route: "UK Biobank",
    title: "多病共存与生活方式队列研究",
    excerpt: "这类论文最有说服力的地方，不在于数据量大，而在于它证明公开 cohort 可以支撑足够严肃的临床与公卫问题。",
    why: "如果你的题目有清晰暴露、长期结局和亚组分析需求，这通常是一条高价值但需要审批的数据路径。",
    sampleQuery: "我想做多病共存与生活方式的队列研究",
  },
  {
    journal: "Nature", impact: "64.8", route: "TCGA",
    title: "公共癌症组学如何支撑机制与预后叙事",
    excerpt: "公开癌症组学最重要的信号不是「能下载」，而是它已经被用来支撑分型、机制、预后和转化医学叙事。",
    why: "如果你正在做肿瘤预后、signature 或免疫治疗反应，TCGA / GEO 是需要优先评估的组学 route。",
    sampleQuery: "TCGA GEO 怎么起肿瘤预后题",
  },
  {
    journal: "Nature Medicine", impact: "82.9", route: "UK Biobank",
    title: "大规模 biobank 如何支撑风险分层与轨迹研究",
    excerpt: "这类论文会直接改变用户对公开数据的判断：没有自建队列，不代表做不了严肃临床问题，关键在于路线选得对不对。",
    why: "适合要做 population-scale risk stratification、trajectory 或 multimorbidity 的研究者。",
    sampleQuery: "UK Biobank 适合什么心血管题",
  },
  {
    journal: "The Lancet Public Health", impact: "64.0", route: "DHS",
    title: "标准化 household survey 能做国际比较与政策叙事",
    excerpt: "这类证据卡说明公开数据不只是单国分析，标准化 survey 还能支撑 LMIC、妇幼与可及性等更偏政策导向的问题。",
    why: "如果你的研究问题天然适合跨国对比，DHS 是不能忽略的一条 route。",
    sampleQuery: "DHS 适合什么公共卫生问题",
  },
  {
    journal: "Nature Biotechnology", impact: "46.9", route: "ClinicalTrials.gov",
    title: "并不是所有高质量研究都从原始 patient-level data 开始",
    excerpt: "Trial registry 这类公开证据库提醒用户：evidence map、设计情报和 landscape analysis 也是正经发表路径，不必强行追求原始数据。",
    why: "如果你现在更需要快速起题和设计判断，这条 route 的启动速度通常快于受控 patient-level 数据。",
    sampleQuery: "我想做系统综述和 meta analysis",
  },
  {
    journal: "J Clin Oncol", impact: "45.3", route: "SEER",
    title: "Registry 规模数据支撑肿瘤结局与生存分析",
    excerpt: "Registry 路线的价值非常直接：分期、生存、亚组、时间趋势，这些都天然适合高可发表性的肿瘤结局问题。",
    why: "如果你要做 population-level oncology，SEER 往往比散乱临床样本更稳定。",
    sampleQuery: "SEER 适合什么肿瘤研究",
  },
  {
    journal: "Lancet Digital Health", impact: "30.8", route: "MIMIC-IV",
    title: "开放 ICU 记录支持风险预测与表型研究",
    excerpt: "这类卡的价值不是告诉用户「MIMIC 很强」，而是明确它适合什么问题、有哪些门槛、什么时候值得投入时间。",
    why: "如果你要做 ICU 风险预测或表型，MIMIC-IV 是高价值路径，但 credentialing 和时间事件处理是摩擦点。",
    sampleQuery: "我想用 MIMIC-IV 做 ICU 风险预测",
  },
  {
    journal: "Gut", impact: "23.0", route: "GEO",
    title: "转录组复用可以做 biomarker 与机制链条",
    excerpt: "GEO 的说服力在于：它不是把表达矩阵下载下来就结束，而是能够支撑 signature、验证链条和机制叙事。",
    why: "如果你更偏向组学复用和快速起题，GEO 往往是比受控库更快的入口。",
    sampleQuery: "TCGA GEO 怎么起肿瘤预后题",
  },
  {
    journal: "JAMA Network Open", impact: "13.8", route: "NHANES",
    title: "全国代表性 survey 数据也能做出规范流调论文",
    excerpt: "NHANES 类型研究的意义在于提醒用户：公开 survey 并不是二流选择，它非常适合规范、可解释、启动快的流行病学问题。",
    why: "如果你优先考虑今天就能开始，NHANES 往往比审批型 cohort 更适合作为第一步。",
    sampleQuery: "NHANES 能不能做肥胖与睡眠",
  },
];

const EvidenceGrid = ({ onSampleClick, limit }) => {
  const cards = limit ? EVIDENCE.slice(0, limit) : EVIDENCE;
  return (
    <section className="mr-evgrid-wrap">
      <div className="mr-section-head">
        <div>
          <div className="mr-kicker">EVIDENCE WALL · 真实公开数据发表</div>
          <h2 className="mr-evgrid-wrap__h2">同样是公开数据，别人已经发到哪里了？</h2>
        </div>
        <div className="mr-section-head__note">9 papers · IF {EVIDENCE[EVIDENCE.length - 1].impact}–{EVIDENCE[0].impact}</div>
      </div>
      <div className="mr-evgrid">
        {cards.map((c, i) => (
          <EvidenceCard key={i} card={c} onSampleClick={onSampleClick} />
        ))}
      </div>
    </section>
  );
};

window.EVIDENCE = EVIDENCE;
window.EvidenceGrid = EvidenceGrid;