Distribution of papers on COVID-19 in the field of anesthesiology in individual countries and journals.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.059 | 0.078 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".