FlowingFlowing
← 返回全部开发案例
开发阶段案例 · 模型盲评尚待独立人工复核，不代表正式 Benchmark 结论。
自动补全Evidence 更优90 / 162 · 7e6fc27444b1cd70

Correcting for partial verification bias in diagnostic accuracy studies: A tutorial using R

统计学 · 2509.12217v1

FLOWING EVIDENCE BENCHMARK

怎么判断 Evidence 真的有帮助？

核心问题是：在同一写作位置、使用同一模型和任务时，提供检索文献片段会怎样改变首次输出？我们成对比较两种条件，保留持平、不可用和评审未完成的结果。

同一段原稿 · 比较是否提供 Evidence

01 · 固定写作位置

论文原稿

同一处写作点位 ▌

同一原稿位置的自动补全成对比较

02 · 构造两组输入

两组共用

原稿上下文、模型、任务和提示词

A · 提供 Evidence

额外提供检索文献片段

B · 不提供 Evidence

不提供检索文献片段

LLM

同一模型、同一版本

A → 首次输出

B → 首次输出

盲评 Agent

匿名标记两份首次输出为 X、Y

按续写质量判断：准确性、任务贴合度和可用性
输出：X 更优 / 持平 / Y 更优 / 两组均不可用

换序复评：X / Y → Y / X

盲评具体怎么判？

① 匿名两份输出
评审看到同一原稿和两份首次输出，但不知道哪份用了 Evidence。

② 比较并换序复评
根据当前任务的标准，按 X/Y、再按 Y/X 的顺序各评一次。

③ 复核分歧
两次结论不一致时再做第三次判定；未完成的评审也留在分母。

自动补全点位怎样分层？

在查看生成结果前，先核查检索片段是否含有能直接支撑下一步续写的具体命题；有则放在左侧机会组，否则放在右侧普通组。每个学科从可准入论文中均衡选取两组点位。50/50 是实验设计，不代表真实写作中两类点位各占一半。

AUTOCOMPLETE · 110

定量生物学、统计学、天体物理学

左侧 55 个、右侧 55 个点位；统计学采用 10 篇论文的复测结果。

AUTOCOMPLETE · 52

心理学与气候科学

左侧 26 个、右侧 26 个点位；心理学计入 9 篇，气候科学计入 4 篇。

表格里的百分比怎么算？

五学科共有 81 个左侧点位。原始盲评有 51 个 Evidence 胜出；任务校验将其中一条空白续写改归“两组均不可用”，因此公开统计为 50 个。普通组另有一条双方空白，已从“未分出胜负”改归“两组均不可用”。这些原始判定仍可在案例页查看。

50Evidence 版本更优
÷
81该层全部点位
=
62%该层 Evidence 获评更优的比例

来源贡献是另一项复核：已进入复核的 21 个 Evidence 获胜点位中，16 个确认直接使用了检索论文；另有 29 个胜出点位尚待复核。

当前是开发阶段的模型评审结果，尚未完成独立人工复核；这些数字不代表正式 Benchmark 结论，也不能单独证明因果关系。

原稿写作位置

原文摘录 · 非 PDF 页面

研究论文 · 原文片段

Correcting for partial verification bias in diagnostic accuracy studies: A tutorial using R

1 Introduction

It is essential to obtain valid estimates of accuracy measures to ensure the clinical validity of the tests. [ 7 ] Biased estimates are misleading, which may result in the premature implementation of the new tests and lead to wrong decision making by the clinicians. [ 15 , 4 ] However, the assessment of index tests in diagnostic accuracy studies often suffer from bias because the verification of the target outcomes by gold standard tests can be clinically infeasible due to cost, ethical and clinical considerations, as well as time-consuming and invasive procedures. [ 4 , 9 , 16 , 5 ]

…more likely to be verified by the gold standard test because they are presumably more likely to be positive for the target outcome. In contrast, those with a negative index test result are less likely to be selected in the verification sample because they are presumably less likely to be positive for the target outcome. Selective sampling may also depend on other factors (covariates), such as gender, age and the presence of clinical symptoms. [ 6 , 17 ] Specifically, this selective sampling creates a partial verification bias, resulting in biased estimates of accuracy measures. [ 15 , 4 , 5 ]

↓ 此处生成下一段续写

黄色标出写作点位附近的原文；本页根据评测输入重排文本，不是原始 PDF 截图。

同一写作位置的成对对照盲评结果：Evidence 更优

两组使用同一模型、同一写作任务和原稿上下文，仅比较是否提供检索文献片段。下方展示评测记录中的首次输出。

Evidence

同一模型与写作位置 · 提供检索文献片段 · 首次输出

盲评更优

The standard approach to correcting for partial verification bias assumes that the true disease status is missing at random (MAR) given the observed test results and covariates, that is, that selection for verification does not depend on the unobserved disease status once these variables are accounted for.

提供给 Evidence 版本的文献片段

Estimation of the Volume Under the ROC Surface in Presence of Nonignorable Verification Bias ↗

In order to correct for verification bias, the researchers often assume that the selection for disease verification does not depend on the disease status, given the test results and other observed covariates, i.e., they assume that the true disease status, when missing, is missing at random (MAR, Li…

展开完整文献摘录

In order to correct for verification bias, the researchers often assume that the selection for disease verification does not depend on the disease status, given the test results and other observed covariates, i.e., they assume that the true disease status, when missing, is missing at random (MAR, Little and Rubin (2002) ).

无 Evidence

同一模型与写作位置 · 不提供检索文献片段 · 首次输出

本次未获偏好

Partial verification bias arises when only a subset of patients undergoes the gold standard test, and the selection of that subset is related to the index test result. Because patients with positive index test results are more likely to be verified, the verified sample is enriched for disease, and the resulting estimates of sensitivity and specificity are distorted.

评审结论与任务校验

模型盲评判定“Evidence 更优”。下方保留评审原始理由（英文），供核对判断依据。

查看模型评审原始理由（英文）

原始理由中的 Output A = 无 Evidence，Output B = Evidence。

Output B is directly and exactly supported by the supplied source, which states: 'In order to correct for verification bias, the researchers often assume that the selection for disease verification does not depend on the disease status, given the test results and other observed covariates, i.e., they assume that the true disease status, when missing, is missing at random (MAR, Little and Rubin (2002)).' Output B reproduces this claim with proper attribution to the source's content. Output A, while topically related to the draft's discussion of partial verification bias, adds unsupported claims: it states that 'the resulting estimates of sensitivity and specificity are distorted' as a definitive outcome, but the source does not mention sensitivity and specificity being distorted—it discusses MAR assumption for correction. More critically, Output A's claim that 'the verified sample is enriched for disease' is an unsupported inference about disease enrichment; the source does not state this. The draft discusses biased estimates of accuracy measures generally, but Output A's specific mechanism of 'enriched for disease' and the specific distortion of 'sensitivity and specificity' goes beyond what the source supports. Output B maintains strict source fidelity without adding unsupported mechanistic claims.

文献片段是输入材料；出现于此不代表输出使用了它，也不代表它能够支持全部主张。原文与检索片段经过截取；页面没有展示模拟分数或模拟 PDF。