Bibliometric study and visual analysis of postoperative diabetes mellitus in kidney transplant recipients based on WoSCC database
Bibliographic record
Abstract
BACKGROUND: In recent years, the increase of the post-transplantation diabetes mellitus (PTDM) after renal transplantation encourages people to do a lot of research on the disease. This paper conducted a bibliometric study on PTDM related literature to explore the risk factors of diabetes after kidney transplantation, as well as the current status, hotspots and development trends of PTDM research, so as to provide reference for researchers in related fields. METHODS: We searched the Web of Science Core Collection (WoSCC) database for PTDM literature from January 1, 1990, to August 20, 2023, and used VOSviewer, CiteSpace, and the R package 'bibliometrix' to do bibliometric analysis. RESULTS: Obesity, 3 months after transplantation tacrolimus concentration >10 ng/mL, temporary hyperglycemia, delayed graft function, acute rejection is specific risk factors related to PTDM in renal transplant recipients. In addition, 74 countries led by China and the United States published 1546 papers, and the number of PTDM-related publications is increasing every year. Primary institutions included the University of California, Los Angeles, Mayo Clinic, University of Oslo, and University of Toronto. The Journal of Transplantation is the most widely read journal in the subject. The authors with the most published literature are Trond Jenssen and Adnan Sharif, and the most cited author is Kasiske BL. Expectations for continued growth in global PTDM research are increasingly high. Future studies will mainly focus on exploring the risk factors of PTDM and identifying new therapeutic approaches and targets.
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: 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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.149 | 0.185 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".