The Application of Rapid Review in the Field of Medical Research: a Bibliometric Analysis
Notice bibliographique
Résumé
Background In the face of the surge of primary studies within a certain period of time, the traditional time-consuming systematic review method is difficult to provide evidence-based basis for clinical practice. Rapid review (RR), as an extension of systematic review, can integrate existing research in a limited time to meet the need for rapid decision-making. Currently, RR has been widely used in the field of medical research, but its application status remains unclear. Objective To explore the current status and hotspot of RR research by using bibliometric analysis. Method CNKI and WOS databases were searched for the researches in the field of RR application from 2001 to 2023, a visualization analysis was performed on annual publication volume, countries, institutions, authors, journals, and keywords of Chinese and English literature by the bibliometrics software of VOSviewer and CiteSpace. Results A total of 151 articles in Chinese and 1197 in English were included. The publication volume of RR application increased gradually from 2001 to 2023, but the publication volume in foreign was higher than that in China, with more obvious increasing trend. The United Kingdom was the country with the highest publication volume (252), the University of Toronto in Canada was the institution with the highest publication volume (52), and Peking University Third Hospital ranked first in publication volume in China (23). The journal Evaluation and Analysis of Drug-use in Hospitals of China had the highest publication volume in China (22), and the journal BMJ Open had the highest publication volume abroad (42). In China, the author team mainly composed of MEN Peng, ZHAI Suodi and ZHAO Zinan published more research. In foreign, the authors of NUSSBAUMER-STREIT, GARTLEHNER and TRICCO published more studies. The most frequently cited literature in China was mainly about RR application and methodology, rapid assessments of drugs or technologies, and the impact of COVID-19, while the most frequently cited literature in foreign was mainly about the the impact, intervention, and epidemiological factors of COVID-19, or methodological studies of RR. Domestic research hotspots mainly focused on the field of rapid health technology assessment in the safety, efficacy, and cost effectiveness of intervention for chronic or serious diseases. Foreign research hotspots mainly focused on the etiology, intervention, diagnosis, prevention, and impact of COVID-19, and rapid evidence synthesis related to decision-making, such as the safety and effectiveness of the drug intervention in children, health care, cancer treatment or mortality risk in middle-aged and elderly populations. Conclusion At present, there is a great difference in the development of RR application in the medical field at home and abroad. The application of RR in foreign is gradually maturing, but in China, it is still in the preliminary stage. The experience of RR application in foreign can be learned to expand the development of domestic RR application.
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 | 0,159 | 0,413 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,006 |
| Bibliométrie | 0,166 | 0,196 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,015 | 0,010 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».