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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。
自动补全无 Evidence 更优86 / 162 · ed1b16ef56464da0

Causal inference methods for combining randomized trials and observational studies: a review

统计学 · 2011.08047v4

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

Causal inference methods for combining randomized trials and observational studies: a review

8 Conclusion

Overall this data analysis highlights the interest of combining two different data sets, but also some challenges : the need for a good understanding of the common covariates, exposure, and outcome of interest before combining the data sets, different missing data patterns, and poor overlap when considering specific target (sub-)populations.

…izability and transportability of RCT from one population to another. The corresponding rich and prolific literature answers a real practical concern: external validity. Indeed, questions about external validity arise as soon as there are treatment effect heterogeneities in the populations under study. We find that, as any growing scientific field, the ideas are in flux: notations differ, implementations are scattered, and the proposed methods proposed still lack real-world benchmarks, generated hand in hand with practitioners. In addition, many open questions still remain as detailled below.

↓ 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：无 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

本次未获偏好

First, the assumptions underlying these methods deserve closer scrutiny. Positivity, in particular, is not merely a technical condition: when overlap between the trial and the observational sample is limited, generalization is possible only to a restricted population without extrapolation, and deterministic violations must be ruled out on substantive grounds.

提供给 Evidence 版本的文献片段

Integrative analysis of randomized clinical trials with real world evidence studies ↗

The transportability of the outcome mean function from the RCT sample to the RWE sample in Assumption 7 is testable, so potential tests for this assumption can be developed to verify this assumption. Assumption 3 states that all real-world patients have nonzero probabilities of participating the RCT…

展开完整文献摘录

The transportability of the outcome mean function from the RCT sample to the RWE sample in Assumption 7 is testable, so potential tests for this assumption can be developed to verify this assumption. Assumption 3 states that all real-world patients have nonzero probabilities of participating the RCT. This assumption requires the absence of patient characteristics that prohibit participation to the RCT. This assumption holds for the CALGB 9633 trial by its protocol. When the positivity assumption is violated, there is limited overlap in the support of covariate in RCT and RWE samples, the generalization can only be made to a restricted population without extrapolation ( Yang and Ding, 2018a ) . In practice, health scientists should carefully examine the question at hand to eliminate deterministic violations of positivity.

无 Evidence

同一模型与写作位置 · 不提供检索文献片段 · 首次输出

盲评更优

Several directions for future work emerge from this synthesis. First, the development of real-world benchmarks, generated in collaboration with practitioners, would allow the competing methods to be compared under conditions that reflect the complexities of actual data sets. Second, further work is needed to clarify the relative merits of the different identification strategies when their underlying assumptions are only partially plausible.

评审结论与任务校验

模型盲评判定“无 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 = Evidence，Output B = 无 Evidence。

Output A contains an unsupported claim. The source discusses positivity, overlap, and generalization to restricted populations, but only in the specific context of 'transportability of the outcome mean function from the RCT sample to the RWE sample' with particular assumptions (Assumption 3, Assumption 7) and the CALGB 9633 trial. Output A generalizes this to 'the trial and the observational sample' broadly and states that 'deterministic violations must be ruled out on substantive grounds,' but the source says health scientists should 'carefully examine the question at hand to eliminate deterministic violations of positivity'—not that they 'must be ruled out on substantive grounds.' The phrase 'not merely a technical condition' is also unsupported editorializing. Output B, by contrast, directly continues the draft's stated open questions: it echoes the draft's mention of 'lack real-world benchmarks, generated hand in hand with practitioners' and 'many open questions still remain,' without adding unsupported factual claims. Both are coherent paragraphs, but A's unsupported material makes it violate the closed-book constraint.

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。