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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteEvidence version preferred42 / 162 · 8992add39c9d8ca7

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

…resent when the index test is positive) and negative predictive value (NPV, the probability that the disease is absent when the index test result is negative). [ 1 , 2 , 10 ] For index test results in continuous scale, receiver operating characteristic (ROC) curve and area under the ROC curve are used to indicate the accuracy. [ 1 , 2 , 3 , 10 , 9 , 7 ] In addition, the use of accuracy measures is not limited to the evaluation of diagnostic tests in medical research, but is also relevant to evaluate the predictive performance of machine (or statistical) learning methods. [ 11 , 12 , 13 , 14 ]

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 ]

↓ 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

Partial verification occurs when only a subset of study participants undergo the gold standard test, so that the true disease status remains unknown for the remaining participants. Because verification is frequently tied to the index test result, the verified and unverified groups are not exchangeable, and accuracy measures estimated from verified participants alone can be substantially biased.

Passages supplied to the Evidence version

Bayesian ROC surface estimation under verification bias ↗

The Receiver Operating Characteristic (ROC) surface is a generalization of ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories. A complication called the verification bias, meaning that not all subjects have their true disease status verified often occ…

Read full passage excerpt

The Receiver Operating Characteristic (ROC) surface is a generalization of ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories. A complication called the verification bias, meaning that not all subjects have their true disease status verified often occur in real application of ROC analysis. This is a common problem since the gold standard test, which is used to generate true disease status, can be invasive and expensive.

No Evidence

Same model and position · no retrieved passages · first output

Not preferred in this comparison

The product produced no insertable continuation text.

The response was truncated before an insertable continuation was available.

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, well-supported continuation that flows logically from the preceding text. The paragraph introduces 'partial verification' as a specific mechanism explaining why verification bias occurs, directly elaborating on the problem stated in the final sentence of the draft (that gold standard verification can be 'clinically infeasible'). The claim that verification is 'frequently tied to the index test result' and that this creates non-exchangeability leading to bias is supported by the source snippet, which describes verification bias as occurring when 'not all subjects have their true disease status verified' and notes this happens because 'the gold standard test...can be invasive and expensive.' The content is factual, relevant, and requires no editing to insert. Output B is empty, which fails the task requirement to continue the manuscript with one coherent academic paragraph.

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.