[Progress of intensive care unit delirium research from 2010 to 2020: analysis based on knowledge visualization].
Notice bibliographique
Résumé
OBJECTIVE: To explored the progress of intensive care unit (ICU) delirium between 2010 and 2020 based on knowledge visualization analysis. METHODS: The literatures related to ICU delirium included in Web of Sciences (WOS) and China National Knowledge Infrastructure (CNKI) databases from 2010 to 2020 were collected. A bibliometric analysis was performed. The growth trend was showed by Excel 2019 software. The information about country, institution and author were extracted by VOSviewer 1.6.15 for generating cooperative network, to find the main research power and each cooperative relation. At the same time, Citespace 5.0.R1 was used to analyze those high frequency keywords and bursting keywords and build the map of co-citation reference, in order to explore the evolution of research in the field of ICU delirium and the hotspots about this field in recent 10 years. RESULTS: A total of 1 102 Chinese journal articles and 2 422 English "Articles" or "Reviews" from 2010 to 2020 were collected preliminarily, and the number of published literatures increased steadily. In the respect of quality, the impact factors of most articles were concentrated between 2 and 3, and the literatures with impact factor over 5 accounted for 27.9% (337/1 209). According to the knowledge visualization analysis, the United States published most of the related articles (total 1 152) in this field, while the England and Canada ranked second and third respectively, totaling 220 and 204. In terms of the distribution of research institutions, the Vanderbilt University School of Medicine was not only far ahead in the number of publication (n = 149), but more importantly, top three high-impact authors located in this institution. The amount of domestic publications was lower than developed countries, however, the burst index, which reflected the sudden increase, ranked first (7.09), suggesting that the interest and investment of Chinese researchers was increasing recently. The most productive institution in China was Capital Medical University School of Nursing with totaling 23 articles. Wu Ying, who published most Chinese papers (n = 14), belongs to this institution. However, it was a pity that there was no large scientific community be constructed in China, and the cooperation between institutions was deficient. By generating the co-occuring keyword mapping, the research hotpots mainly focused on the prevention, treatment and prevention of delirium in mechanically ventilated patients, the effect of dexmedetomidine and exploring the risk factor of ICU delirium. Finally, the results of co-citation reference analysis showed that Cluster 4 (risk assessment) was still in the process of development, in hence it was the frontier in this domain. CONCLUSIONS: There was a big gap between China and leading countries in the field of ICU delirium research. The main research power was located in the United States, and the trending of future studies mainly focus on delirium-related risk assessment.
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 | 0,005 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,039 | 0,040 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».