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This case was judged by an anonymized AI reviewer and has not been independently reviewed by people; inspect the draft, cards, and both outputs to assess it.
AutocompleteWithout cards better220 / 280 · beu-b91cfb8cea0704cf

Use of copula to model within-study association in bivariate meta-analysis of binomial data at the aggregate level a Bayesian approach and application to surrogate endpoint evaluation

Psychology and cognitive neuroscience · 2004.02007v2

FLOWING EVIDENCE BENCHMARK

How do we compare writing with and without cards?

At the same writing position, the same model produces one first result with cards and one without. All 280 pairs enter the overall anonymized review, which assesses whether a decision not to continue fits the draft.

Overall: 138 with-card wins, 26 ties, 107 no-card wins, 5 pairs where neither was usable, and 4 inconclusive judgments. Both sides wrote text in 218 pairs; 48 had a deliberate stop and 14 included an invalid output.

SAME DRAFT · WITH OR WITHOUT CARDS

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED VERSIONS

SHARED BY BOTH

Draft, writing task, and generation model

A · With cards

Relevant literature cards supplied

B · Without cards

No literature cards supplied

Generation model

Same model and version

A → first output

B → first output

Anonymized AI judge

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

Review again with the answer order swapped

HOW DOES BLIND JUDGING WORK?

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

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 positions and cards selected?

We selected 40 writing positions in each of seven fields, for 280 comparisons. The position and relevant literature cards were set before each comparison, so both versions began from the same draft.

The main result retains every frozen position, including places where cards might not add a useful next move. This avoids selecting cases based on their outputs, but 49% describes the whole cohort, not a win rate where evidence clearly had an opportunity to help. This cohort did not record that opportunity label for each position before generation, so those two groups cannot yet be separated reliably.

CARD USE · 140/280

Reported card use in continuations with text

The model reports whether a card affected its continuation. In 140 of 280 tests it produced continuation text and reported card use. The previous count of 150 included 10 cases without continuation text and has been corrected.

COMPARISON · 70 / 7 / 30

Which written version was better?

Of the 107 reported-use cases where both versions produced text, the blinded AI judge preferred the with-card version 70 times, found 7 about the same, and preferred the without-card version 30 times.

How are the two table percentages calculated?

ADOPTION COVERAGE

140reported use with text
÷
280all positions
=
50%

WITH-CARD VERSION PREFERRED

70with cards better
÷
107comparable tests
=
65%

The percentages use different groups: 50% counts continuations with text reporting card use among all 280 tests; 65% covers the 107 such cases with text on both sides. The generating model reports card use, while an anonymized AI judge compares writing quality.

Card use is self-reported by the generator. The anonymized AI judge sees the draft and source snippets, so source-aware preference remains possible; there was no independent human review. The overall result describes these 280 frozen positions, not a win probability for any writing task.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

Use of copula to model within-study association in bivariate meta-analysis of binomial data at the aggregate level a Bayesian approach and application to surrogate endpoint evaluation

6 Discussion

…thout substantially over/underestimating the heterogeneity parameters and the mean effects resulting also more accepptable coverage probabities than BRMA. There were scenarios where it failed to estimate ρ b \rho_{b} as accurately as BRMA-IB. As explained in the previous paragraphs, this was due to the small size of the studies combined with the high proportions of events. In practice, investigating between-studies association between treatment effects on correlated binary outcomes with proportions of events close to one or zero requires studies with sufficiently large number of participants.

… original binomial scale. BRMA-BC offers a robust framework for meta-analysis of binary outcomes avoiding the use of an unreliable approximation of normality for log odds ratios. In this paper, we used the proposed methodology to improve the evaluation of study level surrogate relationships between treatment effects at both the within-study and the between-study level on two binary outcomes with high proportions of events. This can be particularly useful in diseases where the increased effectiveness of targeted treatments often leads to high numbers of responses and reduced numbers of events.

↓ 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 positionOverall AI review: Without cards better

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.

Read overall AI review rationale

A = with cards · B = without cards

The draft ends with a paragraph discussing BRMA-BC's advantages for binary outcomes with high proportions of events, particularly for surrogate endpoint evaluation. The task is to continue with one coherent academic paragraph. Output A largely restates what was just said in the final visible paragraph (binomial scale, high proportions, unreliable normal approximation, surrogate endpoint evaluation) without adding new content or advancing the argument. It reads as redundant filler. Output B introduces a meaningful new limitation about the copula parameter's interpretability across different marginal models—a substantive methodological caveat that appropriately extends the discussion. The draft had noted estimation challenges with ρ_b; B's interpretational warning follows naturally and adds genuine scholarly value. B is more useful because it makes a novel, relevant point rather than repeating the preceding conclusion.

Evidence

Same model and position · retrieved passages supplied · first output

Not preferred in this comparison

The proposed copula-based approach models the within-study association directly on the original binomial scale, which is particularly relevant when the proportions of events are high and the normal approximation for log odds ratios becomes unreliable. This makes the framework applicable to surrogate endpoint evaluation settings where both outcomes are binary and the within-study correlation cannot be ignored.

Passages supplied to the Evidence version

Card 1 · Multiplicative Effect Modeling: The General Case

Generalized linear models, such as logistic regression, are widely used to model the association between a treatment and a binary outcome as a function of baseline covariates. However, the coefficients of a logistic regression model correspond to log odds ratios, while subject-matter scientists are…

Read full passage excerpt

Generalized linear models, such as logistic regression, are widely used to model the association between a treatment and a binary outcome as a function of baseline covariates. However, the coefficients of a logistic regression model correspond to log odds ratios, while subject-matter scientists are often interested in relative risks. Although odds ratios are sometimes used to approximate relative risks, this approximation is appropriate only when the outcome of interest is rare for all levels of the covariates. Poisson regressions do measure multiplicative treatment effects including relative risks, but with a binary outcome not all combinations of parameters lead to fitted means that are between zero and one. Enforcing this constraint makes the parameters variation dependent, which is undesirable for modeling, estimation and computation. Focusing on the special case where the treatment is also binary, Richardson2017 propose a novel binomial regression model, that allows direct modelin …

Card 2 · Meta-Analysis of Odds Ratios With Incomplete Extracted Data

A typical random effects meta-analysis of odds-ratios assumes binomially distributed numbers of events in a treatment and control group and requires the proportion of deaths to be extracted from published papers. This data is often not available in the publications due to loss to follow-up. When the…

Read full passage excerpt

A typical random effects meta-analysis of odds-ratios assumes binomially distributed numbers of events in a treatment and control group and requires the proportion of deaths to be extracted from published papers. This data is often not available in the publications due to loss to follow-up. When the Kaplan Meier survival plot is available, it is common practice to manually measure the needed information from the plot and infer the probability of survival and then to infer a best-guess of the number of deaths. Uncertainty introduced from theses guesses is not accounted for in current models. This naive approach leads to over-certain results and potentially inaccurate conclusions. We propose the Uncertain Reading-Estimated Events model to construct each study's contribution to the meta-analysis separately using the data available for extraction in the publications. We use real and simulated data to illustrate our methods. Meta-analysis based on the observed number of deaths lead to biase …

Card 3 · Analysis of Large Scale Web Experiments Using Sequences of Estimators

Experimental testing is vital in the optimization of web applications, and as such A/B testing has been widely adopted as a methodology for determining optimal content for many web applications. While some testing platforms provide sequentially valid inferences, a large proportion of online tests st…

Read full passage excerpt

Experimental testing is vital in the optimization of web applications, and as such A/B testing has been widely adopted as a methodology for determining optimal content for many web applications. While some testing platforms provide sequentially valid inferences, a large proportion of online tests still utilize traditional statistical tests that do not allow for interim "peeking" at the data or extending the test past its proposed sample size. In this paper we develop results useful for the sequential analysis of large scale experiments. In particular, the properties of sequences of maximum likelihood and generalized method of moments estimators are examined. This leads to new tests of odds ratios and relative risks for binary outcomes. For continuous and ordinal outcome we develop a test of mean difference and a non-parametric test of Area Under the Curve (AUC). Additionally, multivariate versions of these tests are proposed.

Model-reported card use

The model did not report that any card materially affected this completion.

No Evidence

Same model and position · no retrieved passages · first output

Preferred by blind review

A further limitation concerns the interpretation of the copula parameter itself: although ρ b \rho_{b} captures the between-study association between the two treatment effects, its value depends on the marginal models specified for each outcome, so that comparisons of ρ b \rho_{b} across applications with different link functions or marginal distributions should be made with caution.

Earlier card-use comparison

The earlier diagnostic compared only cases with reported card use and text on both sides; its scope differs from the overall AI review above.

This case does not have both with-card continuation text and a model-reported use record. It remains among all 280 tests but does not enter the reported-use comparison.

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.