Four Decades of Clinical Liver Transplantation Research: Results of a Comprehensive Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: Nearly 40 y have passed since the 1983 National Institutes of Health Consensus-Development-Conference, which has turned liver transplantation (LT) from a clinical experiment into a routine therapeutic modality. Since' clinical LT has changed substantially. We aimed to comprehensively analyze the publication trends in the most-cited top-notch literature in LT science over a 4-decade period. METHODS: A total of 106 523 items were identified between January 1981 and May 2021 from the Web of Science Core Collection. The top 100 articles published were selected using 2 distinct citation-based strategies to minimize bias. Various bibliometric tools were used for data synthesis and visualization. RESULTS: The citation count for the final dataset of the top 100 articles ranged from 251 to 4721. Most articles were published by US authors (n = 61). The most prolific institution was the University of Pittsburgh (n = 15). The highest number of articles was published in Annals of Surgery, Hepatology, and Transplantation ; however, Hepatology publications resulted in the highest cumulative citation of 9668. Only 10% of the articles were classified as evidence level 1. Over 90% of first/last authors were male. Our data depict the evolution of research focus over 40 y. In part, a disproportional flow of citations was observed toward already well-cited articles. This might also project a slowed canonical progress, which was described in other fields of science. CONCLUSIONS: This study highlights key trends based on a large dataset of the most-cited articles over a 4-decade period. The present analysis not only provides an important cross-sectional and forward-looking guidance to clinicians, funding bodies, and researchers but also draws attention to important socio-academic or demographic aspects in LT.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.031 | 0.053 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".