Distribution of papers on COVID-19 in the field of anesthesiology in individual countries and journals.
Notice bibliographique
Résumé
INTRODUCTION: The are many published papers on COVID-19 in the field of anesthesia recently. However, there isn’t any study that indicates what kind of issues countries and journals are focusing on this particular subject. The aim of this paper is to determine the countries and journals that contribute the most to the literature on COVID-19 in the field of anesthesia and also to examine the features that make the difference in the total cited count numbers of related papers. MATERIAL AND METHODS: The search engine of the Web of Science was used for the selection of papers. The search yielded 359 published materials in total. However, 78 (61 Articles, 17 Reviews) of them did not have keywords. Therefore, they were excluded from the analysis. The remaining 196 articles plus 84 reviews, in total 280 papers were examined. In order to examine the differences between published materials in terms of total cited count numbers, independent samples t-tests and one-way Anova test were performed with SPSS. In order to explore the topical differences, the keywords according to country of the first author, and the journal were mapped. KNIME and FactoMiner software were used for the analysis. RESULTS: Results indicated that international papers were cited more compared to domestic papers; multi-centered national papers were cited more compared to single-centered national papers. The largest percentage (34.64%) of the overall publications originated from Anglo-American countries (USA=13.93%; England=12.14%; Canada=6.07%; Australia=2.50%). The keyword mapping showed that COVID-19, SARS-CoV-2, Pandemic, Anesthesia, Airway, Acute Respiratory Distress Syndrome, Critical Care, Intensive Care, Personal Protective Equipment, Infection, Mortality, and Mechanical Ventilation were the main keywords of these published materials. CONCLUSIONS:This paper not only showed the features of papers that are cited more but also showed the ranking of countries that contribute the most to the literature and reflected the hot topics about COVID-19 in the field of anesthesia. Extensive studies about COVID-19 have already begun, and the number of studies keep increasing. Therefore, this study could provide hints for authors who would like their papers to be cited more as well as useful information for further research.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,005 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 tête enseignante, 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 ».