Bibliometrics analysis and knowledge mapping of pertussis vaccine research: trends from 1994 to 2023
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.033 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it