Evaluating Approaches for Identifying Retracted Articles and Retraction Notices in Systematic Review Searching
Notice bibliographique
Résumé
Caitlin J. Bakker,1,2 Erin E. Reardon,3 Nicole Theis-Mahon,4 Sara Schroter,5,6 Lex Bouter,7,8 Maurice P. Zeegers2 Objective Systematic reviews gather, appraise, and synthesize studies to inform research, practice, and policy. However, the inclusion of retracted articles, which often contain flaws and falsified or fabricated data, undermines the credibility of systematic reviews. Identifying retracted articles is challenging, as they are inconsistently flagged.1,2 Our study validated and compared approaches to identify retracted articles and their retraction notices. Design Our study, guided by an advisory panel of information specialists and researchers, evaluated approaches for identifying retracted publications from 8 health sciences databases (Cochrane Library, Embase.com, Ovid Embase, Ovid Medline, Ovid PsycINFO, PubMed, Scopus, and Web of Science). Using a reference set of 43,544 retracted publications and 27,755 associated retraction notices from Retraction Watch, we identified items found in each database. From August 10 to 14, 2024, we determined how many of the available items could be retrieved using each approach per database. Two search strategies, database indexing, and 2 citation managers were evaluated. The complete methodology, including search strategies, is available in our protocol.3 Recall (sensitivity) was calculated to evaluate identification effectiveness. Results Recall of retracted publications and notices varied across databases and retrieval approaches. Across databases, search strategy 2 consistently achieved the highest recall, with values ranging from 74.6% to 96.9%. Search strategy 1 also performed strongly, particularly in PubMed and Web of Science (both >93%). In contrast, indexing-based retrieval showed variable performance, with high recall in PubMed (94.6%) and Ovid Medline (94.5%) but much lower in Embase.com (40.6%) and PsycINFO (34%). Citation manager tools (EndNote and Zotero) yielded lower recall, with values rarely exceeding 64%. Recall was lowest for Ovid Embase and PsycINFO regardless of method, while PubMed and Web of Science showed the highest recall. https://assets.underline.io/markdown_image/1/image/b3957ae8a92f24f08736a44da92e7d56.png Conclusions There was substantial variability in the ability of databases and retrieval approaches to identify retracted publications and notices. No single approach captured all items, underscoring the need for multiple approaches in an iterative identification process. References 1. Bakker CJ, Reardon EE, Brown SJ, et al. Identification of retracted publications and completeness of retraction notices in public health. J Clin Epidemiol. 2024;173:111427. doi:10.1016/j.jclinepi.2024.111427 2. Boudry C, Howard K, Mouriaux F. Poor visibility of retracted articles: a problem that should no longer be ignored. BMJ. 2023;381:e072929. doi:10.1136/bmj-2022-072929 3. Bakker C, Reardon EE, Theis-Mahon NR, Schroter S, Bouter L, Zeegers M. Validation and comparison of methods to identify retracted publications during information retrieval. Open Science Framework. Cited June 7, 2025. https://osf.io/rwzym/ 1University of Regina, Regina, SK, Canada, caitlin.bakker@uregina.ca; 2Maastricht University, Maastricht, the Netherlands; 3Emory University, Atlanta, GA, US; 4University of Minnesota, Minneapolis, US; 5BMJ, London, UK; 6London School of Hygiene and Tropical Medicine, London, UK; 7Amsterdam University Medical Center, Amsterdam, the Netherlands; 8Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. Conflict of Interest Disclosures Caitlin J. Bakker is cochair of the National Information Standards Organization Communication of Retractions, Removals and Expressions of Concern Standing Committee. No other disclosures were reported. Funding/Support This research is part of an ongoing PhD collaboration between BMJ and the team Meta-Research at Maastricht University (UM) on the responsible conduct of publishing scientific research. BMJ is published by BMJ Group, a wholly owned subsidiary of the British Medical Association. UM is a public legal entity in the Netherlands. This study is part of Caitlin J. Bakker’s self-funded BMJ/UM PhD. No exchange of funds has taken place for this research project. Role of the Funder/Sponsor The authors are wholly responsible for the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the abstract; and decision to submit the abstract for presentation. Additional Information All authors express their own opinions and not necessarily that of their employers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,795 | 0,947 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,008 |
| Méta-épidémiologie (sens large) | 0,015 | 0,023 |
| Bibliométrie | 0,111 | 0,083 |
| Études des sciences et des technologies | 0,010 | 0,008 |
| Communication savante | 0,022 | 0,030 |
| Science ouverte | 0,017 | 0,030 |
| Intégrité de la recherche | 0,009 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».