[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123308-en":3,"doc-seo-123308-105":30,"detail-sidebar-cat-0-en-105":95},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},123308,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","What predicts citation counts and translational impact in headache research - A machine learning analysis","Aims to create the first machine learning models that predict citation counts and the translational impact of headache research, where translational impact is defined as inclusion in guidelines or policy documents. Uses bibliometric data plus publication titles, abstracts, and keywords from 8600 studies across three headache-focused journals through 31 Dec 2017. Gradient boosting best predicts 5-year citation-count intervals, while translational impact is strongest when combining bibliometrics with text-derived information, improving out-of-sample classification quality.","Original Article  \nWhat predicts citation counts and translational impact in headache research? A machine learning analysis  \nAntonios Danelakis 1,2, Helge Langseth 1,2, Parashkev Nachev3, Amy Nelson3, Marte-Helene Bjørk 1,4,5 ,  \nManjit S. Matharu 1,6 , Erling Tronvik 1,7, Arne May 1,8,*  and Anker Stubberud 1,7,*   \nCephalalgia  \n2024, Vol. 44(5) 1–11  \n! International Headache Society 2024 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/03331024241251488](DOI: 10.1177/03331024241251488)[ ](DOI: 10.1177/03331024241251488)[journals.sagepub.com/home/cep](journals.sagepub.com/home/cep)  \nAbstract  \nBackground: We aimed to develop the first machine learning models to predict citation counts and the translational impact, defined as inclusion in guidelines or policy documents, of headache research, and assess which factors are most predictive.  \nMethods: Bibliometric data and the titles, abstracts, and keywords from 8600 publications in three headache-oriented journals from their inception to 31 December 2017 were used. A series of machine learning models were implemented to predict three classes of 5-year citation count intervals (0–5, 6–14 and, > 14 citations); and the translational impact of a publication. Models were evaluated out-of-sample with area under the receiver operating characteristics curve (AUC). Results: The top performing gradient boosting model predicted correct citation count class with an out-of-sample AUC of 0.81 . Bibliometric data such as page count, number of references, first and last author citation counts and h-index were among the most important predictors. Prediction of translational impact worked optimally when including both bibliometric data and information from the title, abstract and keywords, reaching an out-of-sample AUC of 0.71 for the top performing random forest model.  \nConclusion: Citation counts are best predicted by bibliometric data, while models incorporating both bibliometric data and publication content identifies the translational impact of headache research.  \nKeywords  \nArtificial intelligence, prediction, translational, deep learning, neural networks  \nDate received: 16 November 2023; revised: 7 April 2024; accepted: 8 April 2024  \nBackground  \nThe headache research literature is rapidly expanding (1), yet the importance and influence of individual scientific works can be difficult to measure. Citation counts, impact factor and h-index are common metrics of research performance (2) . Such metrics traditionally drive research funding, recruitment and indicate the  \n1 NorHead Norwegian Centre for Headache Research, Trondheim, Norway  \n2Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway  \n3High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK  \n4Department of Clinical Medicine, University of Bergen, Bergen, Norway 5Department of Neurology, Haukeland University Hospital, Bergen, Norway  \n6Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK  \n7Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway 8Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany  \n*Contributed equally as last authors  \nCorresponding author:  \nAntonios Danelakis, NorHead and Department of Computer Science, NTNU Norwegian University of Science and Technology, Sem Sælands vei 9, 7034, Trondheim, Norway.  \nEmail: [antonios.danelakis@ntnu.no](antonios.danelakis@ntnu.no)  \nCreative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License ([https://](https://)  \n[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/)) which permits any use, reproduction ","cbCaitarcNEOUOz7","https://ap.wps.com/l/cbCaitarcNEOUOz7","pdf",1346302,1,11,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion","[{\"question\":\"How is translational impact defined in this headache research study?\",\"answer\":\"Translational impact is defined as a publication’s inclusion in guidelines or policy documents.\"},{\"question\":\"What data sources were used to train the machine learning models?\",\"answer\":\"The models used bibliometric data and the publication text fields (titles, abstracts, and keywords) from 8600 papers in three headache-oriented journals.\"},{\"question\":\"Which model type performed best for predicting citation count intervals?\",\"answer\":\"A gradient boosting model achieved the highest out-of-sample performance for the correct citation count class (AUC 0.81).\"},{\"question\":\"What combination of features worked best for predicting translational impact?\",\"answer\":\"Best performance came from including both bibliometric data and information from the title, abstract, and keywords (top model AUC 0.71).\"}]","What predicts citation counts and translational impact in headache research - 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