Table_1_A bibliometric analysis of RNA methylation in diabetes mellitus and its complications from 2002 to 2022.docx
Bibliographic record
Abstract
Background<p>RNA methylation has emerged as an active research field in diabetes mellitus (DM) and its complications, while few bibliometric analyses have been performed. We aimed to visualize the hotspots and trends using bibliometric analysis to provide a comprehensive and objective overview of the current search state in this field.</p>Methods<p>The articles and reviews regarding RNA methylation in DM and its complications were from the Web of Science Core Collection. A retrospective bibliometric analysis and science mapping was performed using the CiteSpace software to plot the knowledge maps and predict the hotspots and trends.</p>Results<p>Three hundred seventy-five qualified records were retrieved. The annual publications gradually increased over the past 20 years. These publications mainly came from 66 countries led by Canada and 423 institutions. Leiter and Sievenpiper were the most productive authors, and Jenkins ranked first in the cited authors. Diabetes Care was the most co-cited journal. The most common keywords were “Type 2 diabetes”, “cardiovascular disease”, “diabetes mellitus”, and “n 6 methyladenosine”. The extracted keywords mainly clustered in “beta-cell function”, “type 2 diabetes”, “diabetic nephropathy”, “aging”, and “n6-methyladenosine”. N6-methyladenosine (m<sup>6</sup>A) in DM and its complications were the developing areas of study.</p>Conclusion<p>Studies on RNA methylation, especially m<sup>6</sup>A modification, are the current hotspots and the future trends in type 2 diabetes (T2D) and diabetic nephropathy (DN), as well as a frontier field for other complications of DM. Strengthening future cooperation and exchange between countries and institutions is strongly advisable to promote research developments in this field.</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.944 | 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".