Bibliometrics analysis and knowledge mapping of pertussis vaccine research: trends from 1994 to 2023
Bibliographic record
Abstract
PURPOSE: This study aims to use bibliometric methods to explore the evolving landscape, hotspots, and emerging frontiers of pertussis vaccine research, providing deeper insights into the current research landscape and guiding future vaccine development efforts. METHODS: We conducted a comprehensive search of the Web of Science Core Collection database (WoSCC) from January 1, 1994, to December 31, 2023, employing search terms related to vaccination (vacc* or immun*) and pertussis (pertussis, Whooping Cough, Bordetella pertussis, B. pertussis, Bordetella pertussis infection, or B. pertussis infection) in the Title or Author keywords fields. Bibliometrics analysis of pertussis research was performed utilizing the bibliometrix-biblioshiny package in RStudio, alongside CiteSpace and VOSviewer software. RESULTS: In total, 2,623 records were analyzed, comprising 89.63% (n = 2,351) original research articles and 10.37% (n = 272) review articles. The study revealed that academic research on the pertussis vaccine was growing at a rate of 4.64% per year. The United States and Canada lead in the number of publications. GlaxoSmithKline and the Centers for Disease Control & Prevention- United States emerged as leading institutions, with Halperin SA and Locht C as the most active authors. Vaccine was the most influential journal. Most studies focused on vaccine effectiveness duration, vaccination schedules for high-risk groups, and people's attitudes toward vaccination. CONCLUSION: Our analysis showed increasing interest of researchers in pertussis literature, yet current research mainly emphasized expanding vaccine coverage and optimizing strategies, neglecting new vaccine development. This emphasized the need for prioritizing novel pertussis vaccines to tackle the resurgence challenge.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.173 | 0.267 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".