[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85495-en":3,"doc-seo-85495-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85495,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","RegCheck: A tool for structured comparisons between study registrations and papers","Across the social and medical sciences, registration of planned research activities improves transparency and rigour, yet evidence indicates registrations are often not examined against published papers. Manual checking is labour- and time-intensive, since it requires cross-format reading and domain expertise. RegCheck provides an open-source, modular LLM-assisted workflow that supports structured registration–paper comparison, while keeping human judgement verifiable through deterministic verbatim text segments. It includes worked examples, ongoing evaluation, and fully local command-line use to maximise privacy.","arXiv :2601 . 13330v2 [ cs .CL] 13 Jul 2026  \nRegCheck: A tool for structured comparisons between study registrations and papers  \nJamie Cummins* 1,2 , Beth Clarke 1 , Ian Hussey 1 , and Malte Elson 1  \n1 Institute of Psychology, University of Bern  \n2 Bennett Institute of Applied Data Science, University of Oxford  \n* Corresponding [author: jamie.cummins@unibe.ch](author: jamie.cummins@unibe.ch)  \nJuly 14, 2026  \nAbstract  \nAcross the social and medical sciences, researchers recognise that specifying planned research activities in advance (i.e.,“registration”) has benefits for both the transparency and rigour of science. Despite this, evidence suggests that study registrations frequently go unexamined, limiting their utility. In a way this is no surprise: checking registrations against papers is labour-and time-intensive, requiring careful reading across formats and expertise across domains. Generative large language models (LLMs) are promising in enabling these checks, but ad hoc use of commercial chat models is unlikely to support standardised, reproducible, verifiable, and confidential processes. To overcome this, we present RegCheck ([https://](https://)[ ](https://)[regcheck.app](regcheck.app)), an open-source, modular LLM-assisted tool for structured registration–paper comparison for researchers, reviewers, and editors from across the social and medical sciences. Importantly, RegCheck keeps human expertise and judgement in the loop by ensuring that (i) users are the ones who determine which features should be compared, and (ii) all judgements of (in)consistency between registrations and papers are supported directly by deterministic verbatim text segments which users can use to trivially verify all judgements and claims made by the LLM. We illustrate the utility of RegCheck with a worked example based on a hypothetical clinical trial, showing how the software can be used to support comparisons. We additionally discuss ongoing evaluation of RegCheck, as well as how to use the software fully locally using its command line interface to maximise privacy and security. By lowering the practical barrier to registration–paper comparison, RegCheck aims to make consistency checks a routine part of authors’ workflows, peer review, and postpublication evaluation of research – thereby maximising the potential and utility of study registrations.  \nKeywords: Trial registration; preregistration; large language models; embeddings; trustworthiness assessment; open source software  \n1 Introduction  \nThe importance of registering study designs, materials, hypotheses, outcomes, and processing/analysis plans for the transparency and rigour of research has seen increasing recognition in the medical and social sciences in the last 20 years. In clinical trials, registration is a legal requirement according to the regulations governing many countries such as the United States (NIH Policy on the Dissemination of NIH-Funded Clinical Trial Information | Grants & Funding, n.d.) and Europe (Regulation (EU) No 536/2014 of the European Parliament and of the Council of 16 April 2014 on clinical trials on medicinal products for human use, and repealing Directive 2001/20/EC Text with EEA relevance, 2014) . Even when not legally required, registration sees use across the sciences. In general medical research, the creation of registrations (or publication of study protocols) is growing in prevalence (Tan et al., 2019); the same trend can also be seen in economics, with pre-analysis plans (Ofosu & Posner, 2023), and in psychology, with preregistration (and more recently, Registered Reports, Chambers and Tzavella, 2021; Hardwicke et al., 2024) . Although different fields have different labels, norms, and literatures discussing these approaches, for the sake of consistency here, we refer to all of these using the term “registration”.  \nIn principle, these registrations improve the transparency of decision making and plans associated with research programs. 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