Bibliometric Insights into Research Hotspots and Trends in Obesity and Asthma from 2013 to 2023
Bibliographic record
Abstract
INTRODUCTION: Obesity and asthma are closely linked, but the current state of research on this topic and future research directions have yet to be comprehensively explored. This study aims to provide an up-to-date overview of the research landscape in the field of obesity and asthma. METHODS: A bibliometric analysis was conducted using the Web of Science Core Collection database to identify papers published on obesity and asthma between 2013 and 2023. VOSviewer software was utilized for statistical analysis and visualization of collaborative networks, research trends, literature sources, citation analysis, co-citation analysis, and keyword analysis. RESULTS: A total of 3,406 records from 1,010 journals authored by 17,347 researchers affiliated with 4,573 institutes across 117 countries and regions were retrieved. The number of publications and citations increased annually. The USA and China contributed the majority of records. Major nodes in the collaboration network map included Harvard Medical School, Johns Hopkins University, University of Newcastle, Karolinska Institution, University of Toronto, and Seoul National University. Prolific authors included Anne E. Dixon, Erick Forno, Lisa G. Wood, Deepa Rastogi, and Fernando Holguin. Research trends and hotspots focused on metabolism studies, Mendelian randomization, gut microbiome, inflammation response, gene, biomarker research, and comorbidities were identified as potential future research frontiers. CONCLUSION: This study provides a comprehensive overview of the current research status and trends in the field of obesity and asthma. Our findings highlight the importance of understanding collaboration patterns, research hotspots, and emerging frontiers to guide future research in this area.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.150 | 0.245 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".