Questions to the Article: A Global Overview of COVID-19 Research in the Pediatric Field (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED The article, published on 23 July 2021, is well-written and of interest, but remains several questions that are required for clarifications, such as (1) the static choropleth map of collaboration analysis between countries should be dynamically visualized and highlighted by top three countries on their publications and author collaboration characteristics; (2) the research achievements in authors, institutes, and countries should be quantified by author-weighted scheme considering author order in article bylines; and (3) keyword analysis was too simple to identify the difference in article types between countries. We downloaded 2,268 abstracts from the Pubmed database with a search string of (COVID-19[MeSH Major Topic]) AND (pediatrics[Affiliation]), similar to the mentioned study, and displayed (1) choropleth maps highlighted by the most productive and highly author-collaborated countries, and (2)forest plot to identify differences in article types between two countries. The medical subject headings(MeSH terms) were used to denote the article types in articles. We observed that (1) three top productive countries were the United States, Italy, and India; (2) three top countries collaborated the authors affiliated with the US were Canada, the United Kingdom, and Italy; and (3) only the MeSH term of epidemiology presents the difference in article types between the US and India when the top 10 most frequently occurred MeSH terms were compared. We produced the dashboard-type visualizations to provide valuable information for readers. The novel visual representations make data clear with a better understanding of bibliographic analysis. The methods used in this study are recommended for future studies, not just limited to the field of COVID-19 research.
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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.031 |
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".