Bibliographic record
Abstract
Objective: To analyze the development trend and hotspot of tsRNA research based on bibliometrics.Methods: The bibliometrics method and PubMed database were used to search tsRNA literature from January 2013 to October 2024, and visualized the research contents and hotspots in this field through R packet based on Medpulse bibliometrics analysis platform.Results: a total of 314 studies on tRNA were downloaded after screening.The number of articles on tRNA has been increasing year by year, and the three countries with the most researchers are China, the United States, and Canada.The journal with the most published articles is the International Journal of Molecular Sciences, and the journal with the highest citation per article is Cell.A total of 1,856 authors have studied tRNA, with Chen Qi and Zhang Ying publishing the most articles.Conclusion:The research in this field focuses on tumor, diagnosis, biomarkers, miRNA and piRNA.Among them, "tumor", "biomarker" and "miRNA" are important keywords, which provide valuable reference for scholars in this field.It is suggested to actively strengthen international exchanges and cooperation in the future, and use modern sequencing technology to study the mechanism of tsRNA in tumor diagnosis, occurrence and development to achieve higher quality research results.
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How this classification was reachedexpand
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.191 | 0.205 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".