Top 100 Cited Articles on Peritoneal Dialysis: A Bibliometric Analysis
Bibliographic record
Abstract
Background: The purpose of this study is to broaden the understanding of peritoneal dialysis by presenting the most-cited articles pertaining to this subject. Methods: We searched articles on the Web of Science and selected 100 articles according to the frequency of citations. Next, we reviewed the contents of the articles and identified the characteristics of these articles. Results: There are 21 journals in which the top-100 cited articles were published. The names of the journals, in the order in which the articles are cited, are as follows: Kidney International (25 articles), Nephrology Dialysis Transplantation (13 articles), and Journal of the American Society of Nephrology (12 articles). The top 100-cited articles were published in 15 countries. The country with the greatest number of publications was the United States of America (19 articles). The institution with the greatest number of articles (7 articles) was the University Health Network of Toronto. The author who wrote the most number of articles (5 articles) was Davies SJ. Conclusion: This study is the first in the field of nephrology to provide a list of the top-100 cited articles dedicated to peritoneal dialysis. Through this study, the research trends and major academic interests pertaining to peritoneal dialysis would be identified.
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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: 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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.123 | 0.135 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".