Search arXivSearch

arXiv · 2605.12187

A clinical trial engineering firm

Abstract

Paper mills produce fraudulent research manuscripts built on recycled tables and figures, or on entirely fabricated data. A more recent pattern has emerged: apparently genuine trials with real patients, but with manipulated statistical analyses engineered to support regulatory approval while remaining plausible to peer reviewers. This analysis applies the INSPECT-SR trustworthiness framework to 23 randomised controlled trials and post-marketing studies linked to CinnaGen Co., Iran's largest biosimilar manufacturer, and its clinical operations subsidiary Orchid Pharmed. Papers were retrieved from PubMed and assessed against the original study records. A total of 180 problems were identified across nine categories. The five most frequent issues were reporting failures (n=37), arithmetic violations (n=28), design flaws (n=26), registration irregularities (n=25), and statistical errors (n=25). Analysis of the co authorship network shows that trial design, data management, and manuscript preparation were concentrated within the sponsoring organisation. The underlying structural drivers appear to be a convergence of domestic publication incentives, commercial pressure from international sanctions that created demand for domestically produced drugs, and regulatory pathways that require this body of trial evidence. Because this pattern differs fundamentally from classical paper mills, we propose the term clinical trial engineering to describe it. Regulatory bodies, including the European Medicines Agency (EMA), should treat published clinical evidence from this cluster as unverified until independent access to individual participant data is granted

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matthias Wjst. 2026-05-12. A clinical trial engineering firm. https://arxiv.org/abs/2605.12187

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

The illusion of neutrality in metric-based research evaluation

Venue prestige and citation counts are two widely used, albeit imperfect, signals of research quality. When the two signals conflict, evaluators must decide how much weight to assign each. Yet, it remains unknown how researchers across disciplines trade off these two signals when evaluating research outcome. To fill this gap, we surveyed 869 researchers using paired choices between hypothetical departmental hiring rules that assigned different weights to venue prestige and citation counts, asking which would produce better science. Among 795 respondents whose choices were largely internally consistent, choices placed nearly equal aggregate weight on the two signals. This apparent balance concealed substantial individual heterogeneity: nearly two in five respondents occupied the most venue-heavy or citation-heavy intervals. Moreover, respondents on average chose more citation-heavy rules than they believed their departments used in hiring. By revealing the subjective judgments that arise when research indicators conflict, our findings reinforce calls for greater caution when quantitative indicators are used to evaluate research.

cs.DL

Scoring Grant Applications with Large Language Models

Purpose: Assessing grant applications is time-consuming and difficult, adding to the overall burden of academic peer review. Whilst funders are exploring whether AI can help, there is no published research into the accuracy of Large Language Models (LLMs) for scoring contemporary grants. Design/methodology/approach: This study investigates whether six open-weight LLMs (Gemma 3 1B/4B/12B/27B, DeepSeek R1 32B, Qwen 3 32B) can give useful scores for 2267 recent UK Economic and Social Research Council (ESRC), and Engineering and Physical Sciences Research Council (EPSRC) grant applications, comparing them with scores from the original reviewers and funding panel members. Findings: Although the LLM scores are individually inaccurate, when averaged and converted to ranks they correlate positively with expert average scores. The best performing LLM, Gemma 3 27B (10 iterations with varied prompts), had moderate rank correlations with average reviewer scores (mean rho=0.26). Gemma 3 27B's average correlation with individual reviewers was 0.19, which is lower than the inter-reviewer mean correlation of 0.24, suggesting that it scores are slightly weaker than individual reviewer scores. Gemma 3 27B had weak rank correlations with average panel member scores (mean rho=0.17), with lower average correlations with individual panellists (mean rho=0.14), which is substantially lower than the inter-panellist correlation (mean rho=0.38). Whilst the correlations seem too weak to replace expert review at the final panel stage, LLM scores might help with the initial reviewing state, such as by helping identify the weakest proposals for fast-track desk rejections, to replace one human reviewer, or for triangulation to check for bias.

cs.DL

Open Science, Closed Models: How Funding Shapes AI in Science

How funding shapes AI engagement in science is poorly understood despite its structural importance. We analyze 104,226 scientific papers (2018-2025), linking funding acknowledgments to how each paper engages with foundation models: whether it extends a model (fine-tunes or builds on it), uses one without modification, or references models only peripherally. Three findings emerge. First, funding source is associated with the character of engagement: public funding is associated with higher open-weight model engage- ment; private-only funding selectively enables extension with no robust openness association; mixed funding shows nominally the highest open-weight engagement rates in four of the seven largest disciplines. Second, papers acknowledging industry cloud credits are less likely to use open-weight models, consistent with credit programs steering research toward closed models. Third, industry collaboration carries an independent extension premium consistent with internal corporate resources flowing through coauthorship channels invisible to acknowledgment-based measures. This nexus concentrates in Computer Science and Global North collaborations; Global South research is largely excluded. As public funding contracts and industry compute provision expands, scientific AI work shifts toward closed proprietary infrastructure.

cs.DL