A Bibliometric Analysis of Moderna mRNA-1273 Vaccine for COVID-19
Bibliographic record
Abstract
We performed the 1st bibliometric analysis of the Moderna mRNA-1273 vaccine (for COVID-19). On May 28, 2021, the data was retrieved from the Scopus database. In total, two hundred and three (n=203) documents are published about Moderna, majorly comprising of reviews (n=84) and articles (n=66). In all documents (reviews and articles only), 1110 authors have significantly contributed. The documents per author were 0.135, while authors per document were 7.4. The collaborative Index (CI) was 8.34. By Lotka's Law, we provided information about the frequency of authors. For example, 1021 authors were involved in one (n=1) publication. The total number of publications, h-index, m-index, g-index, and total citations for all authors are provided. The highest documents are published by the National Institutes of Health NIH (n-8) & Moderna Therapeutics (n=8 and the National Institute of Allergy and Infectious Diseases NIAID (n=7). Fifty-five (n=55) countries have significantly contributed to all publications. The highest documents are published by the United States (n=74), India (n=30), and the United Kingdom (n=16). By Biblioshiny, the co-authorship network is also presented. All documents are published in 118 different sources, majorly in Vaccines (n=6), Frontiers in Immunology (n=5), and New England Journal Of Medicine (n=4). We also provided the H-index, g-index, and m-index of all sources. The top ten (n-10) most cited documents are briefly discussed, while we provided a general overview of the publications by co-words analysis. On December 18, 2020, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the Moderna COVID-19 (mRNA-1273) vaccine.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.166 | 0.204 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".