Bibliometric analysis of the top-cited articles on islet transplantation
Bibliographic record
Abstract
AIMS: To identify and characterize the top-cited articles in the field of islet transplantation. METHODS: We used the Science Citation Index Expanded database to identify the most frequently cited articles published after 1900. Articles were evaluated using the following characteristics: citation number, publication year, study design, references, country and institution of origin, authorship, and journal. Keyword analysis and citation networks were used to analyze research trends. RESULTS: The most frequently cited articles received between 146 and 2988 citations; the median was 291. All of the most frequently cited articles were published between 1972 and 2012, and 85 articles were published after 1990. The most popular study design involved basic science (75 articles). The leading countries were the United States (US) and Canada, and the leading institutions were the University of Alberta, Canada, and the University of Minnesota, in the US. Journals specializing in diabetes or transplantation published more than half of the articles (n = 53, 52%), with the journal Diabetes publishing the largest number (n = 30). No association was found between a journal's impact factor and the number of top-cited articles it published. There was no correlation between the number of citations and the number of years since publication, authors, participating institutions, or countries involved. Top-cited articles focused on 2 themes: the use of antirejection immunotherapy or biocompatible encapsulations to prolong graft survival, and assessments of the efficacy of islet transplants, in particular, islet allografts. CONCLUSIONS: Our study can help researchers to identify and decipher the characteristics of top-cited articles in the field of islet transplantation. Just as clinically successful allografts are carried out using the Edmonton protocol, autografts and xenografts should be similarly strengthened to solve problems relating to immune rejection and islet sources, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.059 | 0.093 |
| 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 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".