Bibliometric Analysis of Early COVID-19 Research: The Top 50 Cited Papers
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic is rapidly evolving with the number of cases exponentially rising. The research scientific community has reacted promptly as evidenced by an outstanding number of COVID-19 related publications. As the number of scientific publications rapidly rises, there is a need to dissect the factors that lead to highly impactful publications. To that end, the present paper summarizes the characteristics of the top 50 cited COVID-19-related publications that emerged early during the pandemic. METHODS: A systematic search of the Web of Science, Scopus, and Google Scholar was performed, using keywords related to COVID-19 and SARS-CoV-19. Two independent authors reviewed all the search results, screening for the top 50 cited COVID-19-related articles. Inclusion criteria comprised any publication on COVID-19 or the SARS-CoV-2 virus. Data extracted included the type of study, journal, number of citations, number of authors, country of publication, and study content. RESULTS: As of May 29th, the top 50 cited articles were cited 63849 times during the last 4 months. On average, 14 authors contributed to each publication. Over half of the identified articles were published in only 3 journals. Furthermore, 42% and 26% of the identified articles were retrospective case series and correspondence/viewpoints, respectively, while only 1 article was a randomized controlled trial. In terms of content, almost half (48%) of the identified publications reported clinical/radiological findings while only 7 out of the 50 articles investigated potential treatments. CONCLUSION: By highlighting the characteristics of the top 50 cited COVID-19-related articles, the authors hope to disseminate information that could assist researchers to identify the important topics, study characteristics, and gaps in the literature.
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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: Review 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 | high |
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 | 0.017 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.185 | 0.193 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".