HaHafeng
f9ed0c2528
Week 3 Development Summary: - Implement negative sign normalization (6 Unicode variants) - Enhance T-test validation with smart sample size extraction - Enhance SE triangle and CI-P consistency validation with subrow support - Add precise sub-cell highlighting for P-values in multi-line cells - Add frontend issue type Chinese translations (6 new types) - Add file format tips for PDF/DOC uploads Technical improvements: - Add _clean_statistical_text() in extractor.py - Add _safe_float() wrapper in validator.py - Add ForensicsReport.tsx component - Update ISSUE_TYPE_LABELS translations Documentation: - Add 2026-02-18 development record - Update RVW module status (v5.1) - Update system status (v5.2) Status: Week 3 complete, ready for Week 4 testing Co-authored-by: Cursor <cursoragent@cursor.com>
201 lines
19 KiB
Markdown
201 lines
19 KiB
Markdown
# **临床统计特殊符号提取白皮书**
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**用途：** 指导 Python (python-docx) 在提取 Word 表格时进行字符清洗和标准化。
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**核心痛点：** 同一个数学含义，可能由多种不同的编码方式表示。
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## **1\. 希腊字母类 (Greek Letters)**
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这是最容易出现乱码或识别错误的重灾区。
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| **符号** | **含义** | **常见 Unicode** | **Word 中的潜在坑 (Legacy Fonts)** | **处理建议** |
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| ![][image1] | **卡方检验** | \\u03c7 (χ) \+ \\u00b2 (²) | 1\. 字体设为 "Symbol" 的 'c' 2\. 公式编辑器对象 | **正则匹配**：\[\\u03c7\\u03a7\]2? **关键词**：chi-square, chi |
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| ![][image2] | 显著性水平 | \\u03b1 | 字体设为 "Symbol" 的 'a' | 替换为 alpha |
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| ![][image3] | 回归系数/功效 | \\u03b2 | 字体设为 "Symbol" 的 'b' | 替换为 beta |
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| ![][image4] | 总体均值 | \\u03bc | 字体设为 "Symbol" 的 'm' | 替换为 u 或 mean |
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| ![][image5] | 总体标准差 | \\u03c3 | 字体设为 "Symbol" 的 's' | 替换为 std |
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| ![][image6] | 变化量/差值 | \\u0394 (大写) | 字体设为 "Symbol" 的 'D' | 替换为 delta |
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| ![][image7] | 相关系数 | \\u03c1 | 字体设为 "Symbol" 的 'r' | 替换为 rho |
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**⚠️ 提取陷阱：** 很多老旧的 Word 文档（特别是中文期刊投稿）喜欢用 **Symbol 字体**。在 python-docx 提取 text 时，你可能会读到一个普通的英文字母 c，但用户看到的是 ![][image8]。
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* **解决方案**：检查 run.font.name。如果字体是 Symbol，需要建立映射表（c \-\> χ, a \-\> α）。
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## **2\. 数学运算符类 (Operators)**
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| **符号** | **含义** | **常见 Unicode** | **Word 变体** | **处理建议** |
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| ![][image9] | **加减/标准差** | \\u00b1 | \+/-, \+ / \- | 统一标准化为 \\u00b1 |
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| ![][image10] | 小于等于 | \\u2264 | \<=, \=\< | 统一为 \<= |
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| ![][image11] | 大于等于 | \\u2265 | \>= | 统一为 \>= |
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| ![][image12] | 不等于 | \\u2260 | \!=, \<\>, /= | 统一为 \!= |
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| ![][image13] | 约等于 | \\u2248 | \~, \= | 统一为 \~= |
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| ![][image14] | **负号/减号** | \\u2212 (Minus) | \\u002d (Hyphen), \\u2013 (En Dash) | **极高危！** 必须统一替换为标准连字符 \- (\\u002d)，否则 float() 转换会报错 |
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| ![][image15] | 乘号/交互项 | \\u00d7 | x, X, \* | 统一为 x |
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**⚠️ 提取陷阱：** **“负号”是数据清洗中最大的坑**。Word 会自动把连字符（Hyphen）转成破折号（Dash）或数学减号（Minus）。
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* python 代码：value.replace('\\u2212', '-').replace('\\u2013', '-')
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## **3\. 统计学专用标记 (Statistical Notations)**
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| **符号** | **含义** | **形式** | **提取难点** |
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| ![][image16] | **样本均值** | x 上加横线 | 通常是 **Word 公式对象 (OMML)** 或 **域代码 (EQ)**，python-docx 的 .text **读不出来横线**，只能读到 x。 |
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| ![][image17] | 样本率 | p 上加尖帽 | 同上。 |
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| ![][image18] | 决定系数 | R \+ 上标 2 | python-docx 默认读成 R2。**这通常可以接受**。 |
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| ![][image19] | 下标 (如 ![][image20]) | 文本 \+ 下标 | python-docx 默认读成 Xsub。需要识别 font.subscript 属性。 |
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**⚠️ 提取陷阱：** 对于 ![][image16] 这种带修饰符的字符，python-docx 可能只能提取到底座字符 x。
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* **策略**：对于数据侦探来说，通常我们关注的是表头里的 Mean 或 Average 关键词，而不是符号。如果表头只有 ![][image16]，可能需要结合上下文推断。
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## **4\. 拉丁字母的特殊含义 (Latin Context)**
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虽然是普通字母，但在统计学上下文中具有特殊含义，通常以**斜体 (Italic)** 出现。
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| **符号** | **含义** | **易混淆点** |
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| ![][image21] | t 检验统计量 | 容易混淆为时间单位 t (time) 或 吨 (ton) |
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| ![][image22] | F 检验统计量 | 女性 (Female) |
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| ![][image23] | Z 检验统计量 | \- |
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| ![][image24] | P 值 (概率) | 磷 (Phosphorus) |
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| ![][image25] | 样本量 | 牛顿 (Newton) |
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| ![][image26] | 相关系数 | 半径 (radius) |
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| ![][image27] | 回归系数 | \- |
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| ![][image28] | 优势比 | 手术室 (Operating Room), 或者 (or) |
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| ![][image29] | 风险比 | 心率 (Heart Rate) |
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| ![][image30] | 置信区间 | 心脏指数 (Cardiac Index) |
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**⚠️ 提取策略：** 不能只看字符，要看**组合**。
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* P 单独出现且数值在 0-1 之间 \-\> P 值。
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* t 单独出现且数值 \> 0 \-\> t 值。
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* CI 后面跟着括号 (1.2-3.4) \-\> 置信区间。
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## **5\. Python 字符串清洗工具箱 (Cleaner Utils)**
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建议在 DocxTableExtractor 中集成以下清洗函数：
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import re
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def clean\_statistical\_text(text):
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if not text:
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return ""
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\# 1\. 归一化负号 (CRITICAL)
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text \= text.replace('\\u2212', '-').replace('\\u2013', '-').replace('\\u2014', '-')
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\# 2\. 归一化卡方 (Chi-square)
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\# 处理 Symbol 字体的 'c'2 (需配合 run.font 检查，此处仅处理 Unicode)
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text \= text.replace('\\u03c72', 'chi-square')
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text \= text.replace('\\u03c7\\u00b2', 'chi-square')
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text \= re.sub(r'\[Xxχ\]\\^?2', 'chi-square', text) \# 正则匹配常见变体
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\# 3\. 归一化加减号
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text \= text.replace('\\u00b1', '+/-')
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\# 4\. 归一化比较符
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text \= text.replace('≤', '\<=').replace('≥', '\>=')
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\# 5\. 去除不可见字符 (Zero-width space 等)
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text \= re.sub(r'\[\\u200b\\u200c\\u200d\\ufeff\]', '', text)
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return text.strip()
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## **6\. 总结**
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在 Word 提取中，最大的“鬼怪”不是复杂的 ![][image1]，而是：
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1. **假的负号**（导致 float() 崩溃）。
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2. **Symbol 字体**（导致 ![][image2] 变成 a）。
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3. **多段落换行**（上一节已解决）。
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只要处理好这三点，99% 的统计表格都能被正确解析。
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