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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteEvidence version preferred130 / 162 · 742fa9b305431a74

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

Statistics · 2509.12217v1

FLOWING EVIDENCE BENCHMARK

How do we tell whether Evidence helps?

At the same writing position, with the same model and task, how does supplying retrieved paper passages change the first output? We compare matched versions and retain ties, unusable outputs and incomplete reviews.

SAME DRAFT · EVIDENCE ON OR OFF

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED INPUTS

SHARED BY BOTH

Manuscript context, model, task and prompt

A · With Evidence

Retrieved paper passages supplied

B · Without Evidence

No retrieved passages supplied

LLM

Same model and version

A → first output

B → first output

Blind judge agent

First outputs are anonymized as X and Y

Continuation review: accuracy, fit to the writing task and usability
Returns: X preferred / tie / Y preferred / both unusable

Order check: X / Y → Y / X

HOW DOES BLIND JUDGING WORK?

① Anonymize both outputs
The judge sees the same draft and both first outputs without knowing which received Evidence.

② Compare and swap order
The judge applies task-specific criteria in X/Y and then Y/X order.

③ Review disagreements
A third pass resolves disagreements. Incomplete reviews remain in the denominator.

How are autocomplete positions stratified?

Before seeing generation outcomes, we check whether a retrieved passage contains a specific proposition that directly supports the next writing move. Those positions appear in the left opportunity group; the rest are ordinary positions on the right. We select a balanced sample from admitted papers in each field. The 50/50 split is experimental, not a measure of how often either type occurs in writing.

AUTOCOMPLETE · 110

Biology, statistics and astrophysics

55 positions on each side; statistics uses the ten-paper rerun.

AUTOCOMPLETE · 52

Psychology and climate science

26 positions on each side; psychology includes nine papers and climate science four.

How are the table percentages calculated?

Across five fields there are 81 source-opportunity positions. The original blind review preferred Evidence in 51; a task check moved one empty Evidence continuation to both unusable, leaving 50 in public counts. In the ordinary group, another pair of empty outputs moved from no winner to both unusable. Original verdicts remain visible on case pages.

50Evidence version preferred
÷
81All positions in this group
=
62%Evidence preference in this group

Source contribution is a separate review: 16 of 21 Evidence wins entered into source review directly used retrieved papers; another 29 wins await review.

These are development-stage model judgments pending independent human review. They are not formal Benchmark conclusions and do not, on their own, establish causality.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

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

1 Introduction

…ective verification of the patients, which is referred to as partial verification bias. Several methods for correcting partial verification bias are available depending on the scale of the index test, target outcome and missing data mechanism. However, these are not easily accessible to the researchers due to the complexity of the methods. This article aims to provide a brief overview of the methods available to correct for partial verification bias involving a binary diagnostic test and provide a practical tutorial on how to implement the methods using the statistical programming language R.

↓ Next paragraph was generated here

The highlighted text is near the writing position. This is a layout of the evaluation input, not a PDF screenshot.

A matched comparison at the same writing positionJudgment: Evidence version preferred

Both arms use the same model, task and manuscript context. Only the retrieved passages differ. The first recorded output from each arm is shown below.

Evidence

Same model and position · retrieved passages supplied · first output

Preferred by blind review

Methods for correcting partial verification bias typically require assumptions about the missing data mechanism, most commonly that verification is missing at random given the observed test results and covariates. Under this assumption, several bias-corrected estimators have been developed for diagnostic accuracy measures, including nonparametric likelihood-based approaches for the volume under the ROC surface of ordinal tests.

Passages supplied to the Evidence version

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…

Read full passage excerpt

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) ). Under this assumption, there exist few methods to get bias–corrected inference in ROC surface analysis. Chi and Zhou (2008) proposed a nonparametric likelihood–based approach to obtain bias–corrected estimators for ROC surface and VUS of an ordinal diagnostic test.

No Evidence

Same model and position · no retrieved passages · first output

Not preferred in this comparison

The methods discussed in this tutorial are illustrated using a real dataset, and step-by-step R code is provided to facilitate their application in practice.

Judgment and task check

The blinded model review preferred the Evidence version. Its original rationale appears below.

Read the original model judgment

In this rationale, Output A = Evidence; Output B = No Evidence.

Output A provides a coherent continuation grounded in the source material. It accurately describes the MAR assumption and mentions nonparametric likelihood-based approaches for ROC surface analysis, which directly corresponds to the source snippet. The paragraph flows logically from the draft's mention of 'missing data mechanism' and 'methods available to correct for partial verification bias.' Output B introduces 'a real dataset' and 'step-by-step R code' that are not mentioned in any supplied source or the draft. The draft states the article 'aims to provide a brief overview' and 'a practical tutorial,' but does not establish that these components already exist in the manuscript, and the source provides no support for claims about real datasets or R code being provided. This is an unsupported claim about the paper's own content. Additionally, Output B's tense ('are illustrated,' 'is provided') incorrectly presents these elements as already accomplished rather than as aims, and the paragraph is disconnected from the source's actual content about MAR assumptions and bias-corrected estimators.

The passages are input material; their presence does not mean an output used them or that they support every claim. Draft and source passages are excerpted; no simulated scores or PDF appear here.