Bibliometric analysis of lupus nephritis in children from 1999 to 2022: A review
Bibliographic record
Abstract
Lupus nephritis (LN) is a complication of systemic lupus erythematosus and a damaging disease of the kidney. The injury of LN in children is more serious than that in adults. However, the literature in this field is numerous and complex, which brings great challenges for researchers to extract information. The purpose of this study is to carry out bibliometric analysis and visualization of published literatures, and identify current research hotspots and future research trends in this field. Literature was retrieved from the Web Of Science database from 1999 to 2022. The literature was analyzed and visualized using Citespace 6.1.R6, VOSviewer 1.6.18, and Microsoft Excel 2019. A total of 1059 articles were included in this study. In the past 13 years, an increase in the number of publications every year. Brunner HI is the author with the highest number of published and cited papers in this field, followed by Wenderfer SE. The United States and China are the countries with the highest number of published papers. University Toronto is the most productive institution, followed by University Cincinnati. The most prolific journal was Pediatric nephrology (IF 2.67), followed by lupus (IF 2.21). Lupus was cited the most, followed by Pediatric nephrology. The keyword burst showed the earliest and longest burst was antiphospholipid antibody, validation/risk/rituximab/safety is the current research hotspot. The article with the highest number of citations was Hochberg MC 1997 published in Arthritis Rheum. This study provides valuable information summary for the field of LN in children, which is helpful to strengthen the cooperation among countries, institutions and authors, and promote the research in the field of LN in children.
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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: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.101 | 0.129 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
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".