Bibliometric Analysis of Early COVID-19 Research: The Top 50 Cited Papers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.108 | 0.274 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it