A bibliometric and knowledge-map analysis of antibody-mediated rejection in kidney transplantation
Bibliographic record
Abstract
OBJECTIVES: Antibody-mediated rejection (AMR) is a large obstacle to the long-term survival of allograft kidneys. It is urgent to find novel strategies for its prevention and treatment. Bibliometric analysis is helpful in understanding the directions of one field. Hence, this study aims to analyze the state and emerging trends of AMR in kidney transplantation. METHODS: Literature on AMR in kidney transplantation from 1999 to 2022 was collected from the Web of Science Core Collection. HistCite (version 12.03.17), CiteSpace (version 6.2.R2), Bibliometrix 4.1.0 Package from R language, and Gephi (https://gephi.org) were applied to the bibliometric analysis of the annual publications, leading countries/regions, core journals, references, keywords, and trend topics. RESULTS: A total of 2522 articles related to AMR in kidney transplantation were included in the analysis and the annual publications increased year by year. There were 10874 authors from 118 institutions located in 70 countries/regions contributing to AMR studies, and the United States took the leading position in both articles and citation scores. Halloran PF from Canada made the most contribution to AMR in kidney transplantation. The top 3 productive journals, American Journal of Transplantation, Transplantation, and Transplantation Proceedings, were associated with transplantation. Moreover, the recent trend topics mainly focused on transplant outcomes, survival, and clinical research. CONCLUSIONS: North American and European countries/regions played central roles in AMR of kidney transplantation. Importantly, the prognosis of AMR is the hotspot in the future. Noninvasive strategies like plasma and urine dd-cfDNA may be the most potential direction in the AMR field.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.163 | 0.165 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".