AI evidence extraction

Democratizing Scientific Publishing: A Local, Multi-Agent LLM Framework for Objective Manuscript Editing

Rohan Bhansali, Alon Gorenshtein, Brandon Westover, Daniel M. Goldenholz · medRxiv · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.64898/2026.04.13.26350761

Methodology & findings

Study design

Observational case study with validation by expert reviewers.

Sample

N = 3, 9 groups

Primary method

Descriptive statistics (percentages, proportions); inter-rater agreement assessment (90% agreement reported); deterministic re-evaluation using Phase 0 metrics (unspecified statistical tests). No formal inferential statistical testing, hypothesis testing, or software specification mentioned.

Main result

The study found that PAT "generated 540 evaluable suggestions" and "validation by two expert reviewers (R.B., A.G.) confirmed 391 actionable, high-value revisions (90% agreement), achieving a 72.4% overall usefulness accuracy spanning methodological, statistical, and visual domains." Additionally, "deterministic re-evaluation of 126 agent-suggested rewrite pairs using Phase 0 metrics confirmed text improvement: total word count decreased by 25%, passive voice prevalence dropped sharply from 35% to 5%, average sentence length decreased by 24%, long-sentence fraction fell by 67%, and the Flesch-Kincaid grade improved by 17%."

Reports effect sizes.

Research paradigm

empiricist

Author conclusions

The authors conclude that "our validation confirms that systematic, agent-driven pre-submission review drives measurable improvements, successfully converting manuscript optimization from an opaque, manual endeavor into a transparent and rigorous scientific process."

Risk of bias

Selection bias: only three published papers analyzed, all from clinical neurology domain—not representative of all scientific fields; Reviewer bias: only two expert reviewers; no blinding mentioned; Confirmation bias: reviewers may have been biased toward validating tool-generated suggestions; Attrition/outcome reporting: unclear selection criteria for which 126 of 540 suggestions were evaluated for text improvement metrics; Lack of control condition: no comparison to human-only manuscript editing or other existing tools; Selection bias: Only three published clinical neurological papers examined; may not generalize to other manuscript types or disciplines; Reviewer bias: Limited to two reviewers (R.B., A.G.); no information on blinding or inter-rater reliability beyond 90% agreement on subset; Measurement bias: Evaluation metrics (usefulness accuracy, linguistic measures) may not capture all dimensions of manuscript quality

Data: not_statedCode: not_statedExtracted from: pdfAgreement 65%

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