Bibliometric Analysis of Scientific Output Growth in the Field of Lung Transplantation
Bibliographic record
Abstract
Abstract Background Lung transplantation (LT) has recently emerged as a scientifically validated curative therapeutic modality for patients afflicted with end-stage lung disease. This study aimed to conduct a global bibliometric analysis of research articles on LT between 1983 and 2021. Methods Employing the Web of Science database, a bibliometric analysis was conducted to assess the expansion of scientific output within the field of LT. We searched specific bibliometric characteristics such as language, and year of publication, first author, institutional affiliation, main publishing journals, and highly cited articles. Additionally, we made comparisons of the most productive countries. The VOSviewer program and the open-source visualization software Biblioshiny (version 2.0) were used to perform the bibliometric analysis. Results We identified 10,467 articles on LT published between 1983 and 2021, of which 94.898% were published in the Science Citation Index Expanded. The articles were from 101 different research areas. The publications were from 81 different countries globally, and mostly from the United States (41.196%), Germany (7.118%), and Canada (6.372%). The Journal of Heart and Lung Transplantation was the most published journal. Four thousand seven hundred and ninety three of the publications were published in the last 10 years with a 78,781 citation number in total. The highest number of publications and citations was in 2021. Conclusion The majority of cutting-edge research findings are focused on only a few developed nations, and exchanges with emerging nations are still in their infancy. The United States has a strong, commanding position among the active countries in LT.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.134 | 0.166 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".