A Global Overview of COVID-19 Research in the Pediatric Field: Bibliometric Review
Bibliographic record
Abstract
BACKGROUND: Since the beginning of the COVID-19 pandemic, a great number of papers have been published in the pediatric field. OBJECTIVE: We aimed to assess research around the globe on COVID-19 in the pediatric field by bibliometric analysis, identifying publication trends and topic dissemination and showing the relevance of publishing authors, institutions, and countries. METHODS: The Scopus database was comprehensively searched for all indexed documents published between January 1, 2020, and June 11, 2020, dealing with COVID-19 in the pediatric population (0-18 years). A machine learning bibliometric methodology was applied to evaluate the total number of papers and citations, journal and publication types, the top productive institutions and countries and their scientific collaboration, and core keywords. RESULTS: A total of 2301 papers were retrieved, with an average of 4.8 citations per article. Of this, 1078 (46.9%) were research articles, 436 (18.9%) were reviews, 363 (15.8%) were letters, 186 (8.1%) were editorials, 7 (0.3%) were conference papers, and 231 (10%) were categorized as others. The studies were published in 969 different journals, headed by The Lancet. The retrieved papers were published by a total of 12,657 authors from 114 countries. The most productive countries were the United States, China, and Italy. The four main clusters of keywords were pathogenesis and clinical characteristics (keyword occurrences: n=2240), public health issues (n=352), mental health (n=82), and therapeutic aspects (n=70). CONCLUSIONS: In the pediatric field, a large number of articles were published within a limited period on COVID-19, testifying to the rush to spread new findings on the topic in a timely manner. The leading authors, countries, and institutions evidently belonged to the most impacted geographical areas. A focus on the pediatric population was often included in general articles, and pediatric research about COVID-19 mainly focused on the clinical features, public health issues, and psychological impact of the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.222 | 0.255 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".