Bibliometric analysis of bronchopulmonary dysplasia in extremely premature infants in the Web of Science database using CiteSpace software
Bibliographic record
Abstract
Abstract Objectives: To review the literature related to bronchopulmonary dysplasia in extremely premature infants, summarize research hotspots, and report trends. Methods: Relevant articles from 2008 to 2020 were retrieved from the Web of Science Core Collection database, and we extracted the following data: title, abstract, year, keywords, author, organization, journal and cited literature. We downloaded the data into CiteSpace (version 5.7.R3) to summarize countries, institutions, journals, and authors. We visualized the data with a knowledge map, collaborative network analysis, cluster analysis, and burst keyword analysis.Results: We identified 610 articles on bronchopulmonary dysplasia in extremely premature infants. The United States had the most articles on this topic (302 articles), followed by Canada (49 articles) and Germany (44 articles). The top three institutions, high-yield journals, and authors were all from the United States. The most common keywords were neurodevelopmental disorders, active perinatal care, mechanical ventilation, inflammation, pulmonary hypertension, low-dose hydrocortisone, development, and patent ductus arteriosus.Conclusions: This study illustrates the trends and frontiers in the study of bronchopulmonary dysplasia in extremely premature infants. The current hot issues are to identify the high-risk factors of bronchopulmonary dysplasia in extremely premature infants, reasonable hormone use, new cell therapy, and management of complications.
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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.234 | 0.235 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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, 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".